Machine Learning Engineering

Machine Learning Engineering Jobs

140 jobs found

1d ago

Machine Learning Engineer 4 - Machine Learning Experiences (MLX)

Capital One McLean, VA

McLean, VA

1d ago

$197k - $225k/yr Full time Artificial Intelligence Big Data Data Engineer

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Job Description

Capital One is seeking a Machine Learning Engineer 4 to help build and deploy machine learning models and reusable components at scale. This onsite role in McLean, VA focuses on end-to-end machine learning engineering, from modeling and data pipelines through infrastructure, deployment, and Responsible AI governance.

What you’ll do

Design, build, and/or deliver machine learning models and components to solve real business problems in collaboration with Product and Data Science teams
Shape ML infrastructure decisions using expertise across model and data considerations, including feature selection, training, hyperparameter tuning, dimensionality, bias/variance tradeoffs, and validation approach
Address complex challenges by writing and testing application code, developing and validating ML models, and automating tests and deployments
Work in a cross-functional Agile team to create and improve software supporting big data and ML applications
Retrain, maintain, and monitor models in production
Leverage and/or build cloud-based architectures and platforms to deliver optimized ML models at scale
Create optimized data pipelines that supply ML models
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful releases of ML models and application code
Ensure code is well-managed to reduce vulnerabilities, support risk-governed model practices, and follow best practices for Responsible and Explainable AI
Use programming languages such as Python, Scala, or Java

Required qualifications

Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 4 years of experience programming with Python, Java, Golang, or C++
At least 4 years of machine learning experience with industry-standard frameworks PyTorch or Tensorflow, and libraries including Pandas, NumPy, and Scikit-learn
At least 4 years of experience using and operating large-scale distributed systems (Spark, Ray) to prepare AI/ML data
At least 2 years of experience deploying and operating machine learning solutions in production, including operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large-scale containerized ML systems

Technology stack

Python, Scala, Java, Golang, C++, PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn, Spark, Ray, AWS, GCP, Azure, Kubernetes

Preferred qualifications

Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
3+ years of experience optimizing ML algorithms, configurations, and infrastructure
3+ years of experience applying software development best practices, including source control, testing, and code reviews, as well as CI/CD
3+ years of experience building resilient software solutions using pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring and alarms, and preparing incident response plans
3+ years of experience with ML techniques and model types, including supervised, semi-supervised, unsupervised, and reinforcement learning, as well as regression, classification, and clustering; experience with model architectures such as RNNs, CNNs, LSTMs, and Transformers; and experience evaluating accuracy while diagnosing and addressing underfitting and overfitting
3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation
Authored or co-authored a paper on a machine learning technique, model, or proof of concept

Location and salary

This onsite role is located in McLean, VA. The annual salary range is USD 197,300 - 225,100. The position may also be eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI).

Additional information

Capital One expects to accept applications for a minimum of 5 business days
No agencies please
Capital One is an equal opportunity employer (EOE), committed to non-discrimination in compliance with applicable laws
Capital One promotes a drug-free workplace
Capital One will consider applicants with a criminal history in accordance with applicable laws
Capital One does not sponsor new applicants for employment authorization or offer immigration-related support for this position

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1d ago

Senior Manager, Machine Learning Engineer (People Leader)

Capital One Plano, TX

Plano, TX

1d ago

$209k - $239k/yr Full time Manager Ai Ml Artificial Intelligence

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Job Description

Capital One is seeking a Senior Manager, Machine Learning Engineer (People Leader) to lead machine learning engineering efforts and contribute hands-on across the lifecycle of production ML systems. The role is based in Plano, TX (onsite) and includes technical leadership, mentoring, and talent development.

Role Overview

The Senior Manager, Machine Learning Engineer (People Leader) will design, build, deploy, monitor, and govern machine learning solutions at scale. You will also help shape ML infrastructure decisions, improve production operations through automation and monitoring, and ensure models are managed with appropriate risk and governance practices aligned with Responsible and Explainable AI.

Responsibilities

Design, build, and/or deliver ML models and components that address real business problems in collaboration with Product and Data Science teams
Build and scale multi-tenant platforms that support large-footprint ML training and/or serving at scale
Apply ML modeling expertise to guide infrastructure and technical decisions, including model choice, data and feature selection, training approaches, hyperparameter tuning, dimensionality considerations, bias/variance tradeoffs, and validation
Address complex technical challenges through application development, ML model development and validation, and automation of tests and deployments
Collaborate in a cross-functional Agile environment to create and enhance software for big data and ML applications
Retrain, maintain, and monitor ML models in production
Leverage and/or build cloud-based architectures and platforms to deliver optimized ML models at scale
Construct optimized data pipelines to support ML model training and operations
Use CI/CD best practices, including test automation and monitoring, to support reliable deployment of ML models and application code
Maintain secure, well-managed code to reduce vulnerabilities, apply risk-aware governance for models, and follow best practices in Responsible and Explainable AI
Serve as a force-multiplier by balancing deep technical contribution and innovation with mentoring and skill elevation for peers and junior engineers
Recruit, nurture, and retain engineering talent
Stay current with technology trends through experimentation and participation in internal and external technology communities while mentoring others

Required Qualifications

Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn
At least 6 years of experience using and operating large-scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
At least 4 years of experience deploying and operating production ML solutions and production services in the cloud (AWS, GCP, Azure), using Kubernetes for large-scale containerized ML systems
At least 1 year of people leadership experience

Technologies

Python, Java, Golang, C++
PyTorch, Tensorflow
Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes
CI/CD
Responsible and Explainable AI

Preferred Qualifications

Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
3+ years of experience managing and leading an ML engineering team
5+ years of experience optimizing ML algorithms, configurations, and infrastructure
5+ years of experience applying software development best practices including source control, testing, code reviews, and CI/CD
5+ years of experience building resilient software solutions, including pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring and alarms, and preparing incident response plans
5+ years of experience across ML techniques including supervised, semi-supervised, unsupervised, and reinforcement learning, along with model types (Regression, Classification, Clustering) and architectures (RNNs, CNNs, LSTMs, Transformers)
5+ years of experience with training concepts including loss function, hyperparameters, regularization, and evaluating model accuracy and diagnosing issues such as underfitting and overfitting
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

Compensation

Plano, TX (onsite): USD 209,000 - 238,500 per year
McLean, VA: USD 229,900 - 262,400 for Sr. Manager, Machine Learning Engineer
New York, NY: USD 250,800 - 286,200 for Sr. Manager, Machine Learning Engineer
San Francisco, CA: USD 250,800 - 286,200 for Sr. Manager, Machine Learning Engineer

Benefits

Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being

Additional Information

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position
Applications are expected to remain open for a minimum of 5 business days
No agencies please
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws
Capital One promotes a drug-free workplace
Capital One will consider qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries
If you need an accommodation for applying or finding information, contact Capital One Recruiting at 1-800-304-9102 or RecruitingAccommodation@capitalone.com
For technical support or questions about Capital One’s recruiting process, email Careers@capitalone.com
Capital One does not provide, endorse, or guarantee third-party products, services, educational tools, or other information available through this site
Capital One Financial is made up of several different entities (Canada, United Kingdom, Philippines)

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4d ago

Machine Learning Engineer

Nyla Technology Solutions Annapolis Junction, MD

Annapolis Junction, MD

4d ago

$154k - $183k/yr Full time Artificial Intelligence Data Analytics Data Integration

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Job Description

The AI/ML Engineer will design, build, test, and productize machine learning algorithms and models, supporting end-to-end lifecycle ownership from data and training through deployment, monitoring, and continuous improvement. This onsite role in Annapolis Junction, MD also includes integration oversight and serves as a primary point of contact for AI-related initiatives, with possible leadership of distributed technical teams.

Key Responsibilities

Design, create, test, and productize AI/ML algorithms and models to address complex business challenges.
Develop models that support continuous learning and predictive analysis aligned with business logic and customer requirements.
Manage the full model lifecycle, including data pipeline interaction and architecture design, continuous training, deployment, scheduling, monitoring, and performance tuning based on iterative user feedback.
Provide system integration oversight across AI/ML capabilities and related environments.
Act as a primary point of contact for AI-related initiatives.
May lead geographically diverse technical teams.

Required Qualifications

Active security clearance at the TS/SCI polygraph level is required.
Hands-on experience working with various machine learning libraries, packages, and frameworks.
Proficiency in statistical analysis, data selection, application of metrics, and presentation of results.
Practical understanding of model deployment, scheduling, monitoring, retraining, and system integration oversight.
Familiarity with concepts across application development, infrastructure management, data engineering, and data governance.
Exceptional analytical, problem-solving, and interpersonal skills to collaborate with stakeholders and lead distributed teams.
Education and experience: Bachelor’s Degree in Computer Science/Computer Engineering/Software Engineering (or related technical discipline) plus 2+ years of professional software engineering experience, OR High School Diploma/GED plus 5+ years of hands-on technical software development experience in lieu of a degree.

Technology Stack

Python
Java
C
R

Desired Skills

Expert knowledge of multiple programming languages, including Python, Java, C, or R.

Compensation and Benefits

Salary: USD 154,000 - 183,000 per year.

Top-of-market compensation and benefits package
Nyla FLEX program (custom-tailored benefits)
4 weeks of Annual Leave plus 11 holidays, with an additional day of Annual Leave for each year at the company
Flexibility to cash out annual leave hours, opt out of other Nyla benefits, and/or arrange for additional hours on contract (over 40 hrs/week)
Option to earn 1.3 times your hourly rate once you work over 1880 hours on contract
Option to buy additional annual leave for a total of 6 weeks of annual leave (up to 240 hours of leave plus 11 holidays)
Paid anniversary leave
Option to scale down to 30-32 hours while still receiving top-tier Nyla benefits
Health care (medical, dental, vision) 100% covered by the company
10% 401k matching with full vesting day 1
$5,000 per year for Professional Development to be used toward fees, tuition, or time off
Student loan repayment program
8 hours of volunteering annually

About Nyla Technology Solutions

Nyla Technology Solutions delivers AI, Data Science, and Software Engineering services for the U.S. Government. The company is headquartered in Columbia, Maryland and supports a forward-thinking approach to technology development and delivery.

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4d ago

Staff Machine Learning Engineer

Integral Ad Science, Inc. San Francisco, CA

San Francisco, CA

4d ago

$135k - $232k/yr Full time Application Security Artificial Intelligence Automation

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Job Description

Integral Ad Science is hiring a Staff Machine Learning Engineer (Data Science Team) to lead large-scale AI/ML systems and production deployments across the open web, social networks, video, and mobile.

Responsibilities

Set the architectural direction and system design for core AI/ML services
Serve as technical lead for multi-quarter initiatives from inception through global deployment
Design and build large-scale deep learning infrastructure and platforms for distributed model training
Deliver low-latency, high-availability ML systems at enterprise scale
Coordinate with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy
Convert abstract product requirements into concrete technical designs
Mentor and guide senior and mid-level data scientists and engineers
Define code quality expectations, establish standard methodologies, and lead architecture design reviews
Work with large-scale AI training infrastructure components (accelerators, network fabrics, CUDA, NCCL, RDMA)
Integrate with big data and infrastructure ecosystems (Databricks, Spark, Kubernetes, Kafka, Prometheus)
Build and support robust CI/CD pipelines that move models from research to production endpoints reliably

Requirements

PhD/Master’s degree in a technical field (e.g., computer science, mathematics, statistics) or equivalent years of experience
7+ years of machine learning experience in industry with a demonstrated history at Lead, Principal, or Staff level
Deep expertise designing complex ML systems, including ranking infrastructures and high-throughput data pipelines
Production-hardened experience with frameworks including PyT orch, JAX, or T ensorFlow
Strong production-grade programming skills in Python, Go, Java, or C++, with solid software design principles and algorithms knowledge
Ability to operate in ambiguity, take large unstructured problems, and drive them to technical resolution independently
Strong track record of collaborating with, elevating, and mentoring engineering talent

Technologies

PyT orch, JAX, T ensorFlow
Python, Go, Java, C++
CUDA, NCCL, RDMA
Databricks, Spark, Kubernetes, Kafka, Prometheus

Benefits

Paid time off
Health insurance (medical, dental, vision)
PPO, HSA and FSA options
401k with employer matching contributions
Annual bonus and/or other incentive plans (eligible)

Compensation & Location

Location: San Francisco, CA (onsite)
Salary range: USD 135,100 - 231,600 per year

IAS Pay Transparency

Primary Location: US - New York, NY
Primary Location Base Pay Range: $135,100.00 - $231,600.00 Annual
Additional Locations: US - Chicago, IL; US - San Francisco, CA
Additional Locations Pay Range: $119,000.00 - $204,000.00 USD Annual

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4d ago

Principal Machine Learning Engineer, Business Process Optimization

Workday Pleasanton, CA

Pleasanton, CA

4d ago

$191k - $342k/yr Full time Ai Ml Artificial Intelligence Automation

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Job Description

The Principal Machine Learning Engineer will lead architecture and implementation work on next-generation AI capabilities for Workday’s Business Process Optimize team within Admin Central. This role focuses on agentic frameworks, multi-tenant ML infrastructure, and enterprise-grade reliability to support billing integrity across global regions.

Role Focus

Agentic AI & Orchestration: Design, implement, and maintain advanced agentic capabilities, including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
Scalable ML Infrastructure: Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native technologies. Architect multi-tenant runtimes for fast, low-latency inference to millions of global users.
MLOps & Enterprise Reliability: Establish end-to-end MLOps standards, including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails. Ensure systems remain explainable, secure, and production-resilient.
Technical & Strategic Leadership: Collaborate with software engineers, Product Managers, Data Engineering, SRE, and Legal or Compliance stakeholders to convert ambiguous business needs into scalable technical designs and long-term data-governance strategies.

Key Responsibilities

Design, implement, and maintain advanced agentic capabilities, including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native technologies.
Architect multi-tenant runtimes that deliver fast, low-latency inference to millions of global users.
Establish end-to-end MLOps standards, including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails, ensuring systems are explainable, secure, and production-resilient.
Partner with software engineers, Product Managers, Data Engineering, SRE, and Legal or Compliance stakeholders to produce scalable technical designs and long-term data-governance strategies.

Required Qualifications

12+ years in ML development and R&D leadership, including 8+ years with advanced ML methodologies, distributed frameworks, and cloud-based platforms, delivering complex, scalable, production-grade ML systems.
6+ years building and hosting scalable ML services, microservices, and high-throughput data pipelines in production using Java, Go, Scala, or Python.
4+ years working with LLMs, generative AI, RAG pipelines, and autonomous agent frameworks for real-world enterprise applications.
Expertise in high-throughput streaming (Kafka), real-time feature extraction (Spark/Flink), and distributed columnar storage (ClickHouse, Iceberg) to architect large-scale ML pipelines.
Deep knowledge of deep learning, LLM orchestration, RAG, and agentic systems, applying ML algorithms and Model-Based Design (including MCP integrations and tool-use frameworks) for multi-agent coordination, intent classification, and workflow optimization.
Proven ability to lead EDA on multi-tenant agentic workflows, including high-dimensional embeddings, vector database management, and data isolation.
Proven ability to lead model building, fine-tuning, and deployment of domain-specific foundation models, including Kubernetes/Docker deployment, automated CI/CD retraining, and managing drift and latency at scale.
Mastery of Python and the ML ecosystem (PyTorch, TensorFlow, LangChain, LangSmith, LlamaIndex, NumPy, Pandas, Scikit-learn) and strong experience deploying production systems on PostgreSQL, Redis, Kafka, and cloud infrastructure (AWS/GCP/Azure).
Exceptional collaboration and mentoring skills, including technical leadership across cross-functional pods, driving sprint planning, and translating technical and governance decisions for executives.

Education

Bachelor’s degree (or equivalent experience) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Master’s or PhD strongly preferred.

Location and Work Model

Pleasanton, CA (hybrid)

Compensation

USD 190,600 - 342,000 per yearly

Workday Pay Transparency Statement

Primary Location: USA.CA.Pleasanton
Primary Location Base Pay Range: $228,000 USD - $342,000 USD
Additional US Location(s) Base Pay Range: $190,600 USD - $342,000 USD

Flexible Work Approach

Spend at least half (50%) of your time each quarter in the office or in the field with customers, prospects, and partners (depending on role).
Remote “home office” roles may have opportunities to come together in Workday offices for important moments.

Technologies

Python, Kubernetes
Java, Go, Scala
REST services, event-driven data pipelines
CI/CD automation, observability, model monitoring
Privacy and anonymization guardrails
LLMs, generative AI, RAG pipelines
Autonomous agent frameworks, tool calling, multi-agent workflows, long-range planning, reasoning frameworks, human-in-the-loop integrations
Kafka, Spark, Flink
ClickHouse, Iceberg
MCP integrations, Model-Based Design, EDA
High-dimensional embeddings, vector database management
Docker
PyTorch, TensorFlow
LangChain, LangSmith, LlamaIndex
NumPy, Pandas, Scikit-learn
PostgreSQL, Redis, AWS, GCP, Azure

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4d ago

Machine Learning Engineer 5 (Senior Manager, IC)

Information Technology Senior Management Forum New York, NY

New York, NY

4d ago

$230k - $262k/yr Full time Backend Developer Manager Artificial Intelligence

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Job Description

Design, build, and scale production-grade machine learning with a role focused on training and serving platforms, model monitoring, and Responsible and Explainable AI. This onsite position in New York, NY pairs hands-on ML engineering with platform and infrastructure work, including CI/CD, data pipelines, and cloud architectures that support large-footprint model training and deployment.

What you’ll do

Design, build, and deliver ML models and components to solve real business problems in close collaboration with Product and Data Science.
Build and scale massive multi-tenant platforms for large footprint ML training and serving.
Drive ML infrastructure decisions using modeling expertise across model and data selection, feature selection, training, hyperparameter tuning, dimensionality, validation, and bias/variance tradeoffs.
Solve complex engineering problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
Collaborate in a cross-functional Agile environment to create and enhance software for big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful releases.
Ensure well-managed code to reduce vulnerabilities, support risk-governed model governance, and follow best practices in Responsible and Explainable AI.
Use programming languages such as Python, Scala, or Java.

Required qualifications

Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering).
At least 6 years programming with Python, Java, Golang, or C++.
At least 6 years Machine Learning experience with industry standard frameworks PyTorch or TensorFlow and libraries Pandas, NumPy, Scikit-learn.
At least 6 years experience operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data.
At least 4 years deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), using Kubernetes to manage large-scale containerized ML software systems.

Preferred qualifications

Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
5+ years optimizing ML algorithms, configurations, and infrastructure.
5+ years following software development best practices including source control, testing, code reviews, and CI/CD.
5+ years building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation.
5+ years working with ML techniques (Supervised, semi-supervised, unsupervised, reinforcement learning) across model types (Regression, Classification, Clustering), architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy to diagnose underfitting and overfitting.
5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
Ability to communicate complex technical and ML concepts clearly to a variety of audiences.

Technologies

Python, Scala, Java, Golang, C++
PyTorch, Tensorflow
Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes

Salary: USD 229,900 - 262,400 per year. Experience: minimum 4 years. Location: New York, NY (onsite).

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4d ago

Machine Learning Engineer 5, Senior Manager - Intelligent Foundations and Experiences (IFX)

Information Technology Senior Management Forum McLean, VA

McLean, VA

4d ago

$230k - $262k/yr Full time Backend Developer Manager Ai Ml

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Job Description

This role is for a Senior Manager focused on building and scaling AI and machine learning capabilities, including model training and serving in production and multi-tenant ML platforms. You will work with Product and Data Science teams to deliver reliable data pipelines, CI/CD workflows, and Responsible and Explainable AI practices.

Location

McLean, VA (onsite)

Salary

McLean, VA: $229,900 - $262,400 per year (Machine Learning Engineer 5)
Richmond, VA: $209,000 - $238,500 per year (Machine Learning Engineer 5)

Role Summary

You will design and deliver ML models and platform components that address business needs, while using your depth in modeling and ML infrastructure decisions. The work includes training, deploying, and maintaining models in production, scaling large footprint training and serving, and ensuring governance and security best practices for risk and Responsible and Explainable AI.

Responsibilities

Design, build, and/or deliver ML models and components to solve real-world business problems in collaboration with Product and Data Science teams
Build and scale multi-tenant platforms enabling large footprint ML training and/or serving at scale
Use ML modeling fundamentals to guide infrastructure decisions, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate in a cross-functional Agile team to create and enhance software for big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Apply CI/CD best practices, including test automation and monitoring, to support successful model and application deployments
Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML follows Responsible and Explainable AI best practices
Use programming languages such as Python, Scala, or Java

Required Qualifications

Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries Pandas, NumPy, Scikit-learn
At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning systems

Technologies

Python, Scala, Java, Golang, C++
PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes, CI/CD

Benefits

Eligible for performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being

Preferred Qualifications

Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
+2 years experience building production RAG pipelines (chunking, embeddings, vector indexing, graph RAG, or fine-tuning)
+2 years experience leveraging Agentic patterns (Tool Calling, ReAct loops, Plan-and-Execute, Multi-Agent Collaboration)
+2 years with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and model APIs (OpenAI, Anthropic, Bedrock, Ollama, Hugging Face)
+2 year experience in building agentic AI systems and agentic workflows
+2 year experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang
+2 years experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs)
5+ years of experience optimizing ML algorithms, configurations, and infrastructure
5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans
5+ years of experience working with Machine Learning techniques and model types, including evaluation and diagnosing issues such as underfitting and overfitting
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences