Machine Learning Infrastructure

Machine Learning Infrastructure Engineer Jobs

15 jobs found

Today

Evaluation & Insights Machine Learning Engineer

Apple Cupertino, CA

Cupertino, CA

Today

$185k - $325k/yr Full time Ai Evaluation Ai Testing And Evaluation Artificial Intelligence

Apply Now

Job Description

On Apple’s Human-Centered AI team, this role focuses on evaluating and improving AI systems using data-driven analysis, model behavior study, and qualitative insights that translate into engineering action.

Location

Cupertino, CA (onsite)

Compensation

USD 184,700 - 324,800 per year. Base pay depends on skills, qualifications, experience, and location.

Role Summary

The Evaluation & Insights Machine Learning Engineer designs evaluation approaches to assess LLMs and multimodal systems. The engineer builds evaluation frameworks, analyzes AI outputs, identifies failure patterns, and communicates findings to product and engineering teams to support model improvements.

Key Responsibilities

Architect and deliver comprehensive evaluation suites for LLMs and multimodal models, with focus on edge cases across multi-step reasoning, factuality, adversarial robustness, safety, and alignment.
Create deterministic, heuristic, and LLM-assisted evaluation frameworks (including LLM-as-a-judge and reward modeling) to measure human-perceived quality metrics such as helpfulness and hallucination rates.
Convert qualitative failure modes into quantifiable loss patterns, programmatic guardrails, and actionable data-mixture adjustments for model training and inference.
Work with engineering teams to refine model behavior using evaluation telemetry to guide prompt engineering, Retrieval-Augmented Generation (RAG) strategies, and model fine-tuning.
Use advanced ML methods such as embedding-based clustering, representation learning, and perturbation analysis to map error taxonomies and latent failure manifolds.
Build MLOps workflows that codify evaluation metrics, automate regression testing across model checkpoints, and integrate human-centric assessments into ML CI/CD pipelines.
Design scalable, distributed inference and processing pipelines (for example, Ray and vLLM) to support high-throughput evaluation, automated annotation, and large-scale output analysis.
Define quantitative evaluation frameworks covering nuanced human factors, including trust calibration, conversational state tracking, and interpretability.
Develop automated evaluation pipelines using LLMs to score outputs at scale, with optimization for strong correlation to human baseline annotations.
Collaborate with ML researchers, software developers, and product managers to turn product requirements into reliable and efficient evaluation infrastructure.

Required Qualifications

Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field.
8+ years of relevant industry experience in ML Engineering or Applied Research.
Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face).
Proven experience building scalable ML inference pipelines, model-evaluation workflows, and structured rating frameworks for large-scale AI systems.
Ability to interpret unstructured model outputs (text, transcripts, embedding spaces) and synthesize qualitative findings into actionable engineering guidance and training objectives.
Hands-on experience developing, fine-tuning, or evaluating LLMs, multimodal models, and NLP systems.
Deep familiarity with AI quality metrics, hallucination detection techniques (for example, SelfCheckGPT), model alignment approaches (such as RLHF and DPO), and LLM-as-a-judge frameworks (for example, G-Eval and DeepEval).
Experience building internal tools or automated pipelines for ML workflows using platforms such as MLflow and Weights & Biases (or similar).
Strong familiarity with advanced prompt engineering, RAG architectures (vector databases and semantic search), and Fine-Tuning.

Preferred Qualifications

Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.

Technologies & Tools

Python, PyTorch, JAX, Hugging Face
LLM-as-a-judge, reward modeling
Retrieval-Augmented Generation (RAG), vector databases, semantic search
Embedding-based clustering, representation learning, perturbation analysis
MLOps, CI/CD pipelines
Ray, vLLM
Trust calibration
SelfCheckGPT, RLHF, DPO
G-Eval, DeepEval
MLflow, Weights & Biases
Fine-Tuning

Benefits

Comprehensive medical and dental coverage
Retirement benefits
A range of discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary restricted stock unit awards
Opportunity to purchase Apple stock at a discount through the Employee Stock Purchase Plan
Opportunity to progress as you grow and develop within a role
Comprehensive total compensation package may include discretionary bonuses or commission payments as well as relocation

Additional Notes on Pay and Programs

Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. This role might be eligible for discretionary bonuses or commission payments and relocation.

View full job page →

Today

Machine Learning Engineer

XPO Boston, MA

Boston, MA

Today

$100k - $120k/yr Full time Artificial Intelligence Automation Bigquery

Apply Now

Job Description

XPO is hiring a Machine Learning Engineer to help build and sustain the machine learning foundation behind training, evaluation, and deployment. This hybrid role is based in Boston, MA, with a focus on reliable data preparation and validation, ML infrastructure, and MLOps capabilities that support production performance over time.

The position includes hands-on work across the ML lifecycle, from building pipelines that ensure model-ready data to implementing CI/CD workflows for ML models. You will also help connect experimentation to deployment by partnering with applied and data scientists and collaborating closely with data engineering teams.

Role summary

Build and maintain ML data preparation and validation tooling to support high-quality inputs for ML and optimization models
Design and implement ML infrastructure for model training, evaluation, and deployment
Strengthen MLOps with CI/CD, monitoring, and feedback loops to track production performance
Contribute to shared MLOps tooling and best practices across the AI/ML organization

Responsibilities

Develop and maintain data preparation and validation tooling to improve the quality of ML and optimization model inputs
Create ML infrastructure that supports training, evaluation, and deployment workflows
Build and maintain CI/CD pipelines for machine learning models, including automated testing and validation
Implement model monitoring, drift detection, and feedback loops to measure and improve production performance
Partner with applied and data scientists to productionize models and streamline movement from experimentation to deployment
Work with data engineering teams to ensure data pipelines are reliable and accessible
Support shared MLOps tooling and organizational best practices

What you’ll do on a typical day

Build and maintain data preparation and validation tooling
Design and implement ML infrastructure for training, evaluation, and deployment
Maintain CI/CD pipelines for machine learning models with automated testing and validation
Implement monitoring, drift detection, and feedback loops for production models
Collaborate with applied and data scientists to productionize models
Coordinate with data engineering teams to ensure dependable, accessible data pipelines
Contribute to shared MLOps tooling and best practices

Requirements

Bachelor's degree in Computer Science, Engineering, or related field, or equivalent related work or military experience
1 year of experience in software or machine learning engineering, including hands-on experience building data pipelines, ML infrastructure, or MLOps tooling
Experience developing data preparation, validation, or quality-checking tooling for ML pipelines
Proficiency in Python and SQL
Experience with cloud data or ML platforms (examples: AWS, GCP, BigQuery)
Strong collaboration skills partnering with data science/applied science teams and data engineering teams
Master's degree in Computer Science or related field
3+ years of experience building ML infrastructure for training, evaluation, and deployment at scale
Experience building and maintaining CI/CD pipelines for ML models
Experience with model serving and inference infrastructure (batch and real-time)
Experience implementing model monitoring, drift detection, and feedback-loop tooling
Experience with containerization and orchestration (Docker, Kubernetes)
Experience partnering with data engineering teams on data pipeline reliability and access

Technologies

Python
SQL
AWS
GCP
BigQuery
Docker
Kubernetes

Compensation and location

Location: Boston, MA (hybrid)
Annual salary range: USD $100,000 - $120,000 per year
Actual compensation may vary based on experience and skill set
This is an incentive-based position, which may include bonuses, incentive or commission plans

Benefits

Competitive compensation package
Full health insurance benefits available on day one
Life and disability insurance
Earn up to 15 days of PTO over your first year
9 paid company holidays
401(k) option with company match
Education assistance
Opportunity to participate in a company incentive plan

View full job page →

1d ago

Sr. Lead AI Engineer

Capital One McLean, VA

McLean, VA

1d ago

$230k - $262k/yr Full time Artificial Intelligence Data Platform Engineering

Apply Now

Job Description

Build responsible, high-impact AI systems with Capital One’s Intelligent Foundations and Experiences (IFX) team. In this onsite role in McLean, VA, you’ll help design, develop, deploy, and continuously improve foundational AI and LLM software components that support how associates work and how customers interact with Capital One.

What you’ll do

Partner with cross-functional leaders across engineering, research science, technical program management, and product management to deliver AI-powered capabilities.
Design, build, test, deploy, and support AI software components, including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Work with a broad mix of Open Source and SaaS AI technologies, including AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
Introduce and refine LLM optimization techniques to improve scalability, cost, latency, and throughput for large-scale production AI systems.
Help shape the team’s technical vision and contribute to the long-term roadmap for foundational AI systems.

What you bring

Education + experience: a Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 6+ years developing AI/ML algorithms or technologies, or a Master’s degree plus 4+ years.
Programming: at least 6 years with Python, Go, Scala, or Java.
Cloud deployment: 7+ years deploying scalable and responsible AI solutions on AWS, Google Cloud, Azure, or equivalent private cloud.
Experience designing, developing, integrating, delivering, and supporting complex AI systems.
Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders.
Experience developing AI/ML algorithms (for example: LLM inference, similarity search, VectorDBs, guardrails, memory) using Python, C++, C#, Java, or Golang.
Hands-on experience applying state-of-the-art optimization techniques for training and inference to improve hardware utilization, latency, throughput, and cost.
Strong communication skills to explain complex AI concepts clearly to peers.

Tools and technologies

AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, AWS, Google Cloud, Azure, Python, Go, Scala, Java, C++, C#, Golang, foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, observability.

Compensation and benefits

Salary (McLean, VA, onsite): USD 229,900 - 262,400 per year.

Sales territory: USD 209,000 - 238,500 for Sr. Lead AI Engineer.

Other locations: New York, NY (USD 250,800 - 286,200), San Jose, CA (USD 250,800 - 286,200).

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

About the team

The Intelligent Foundations and Experiences (IFX) team is central to bringing Capital One’s AI vision to life. The team partners across the company to advance the state of the art in science and AI engineering, building and deploying proprietary solutions that deliver value to millions of customers. AI models and platforms from IFX help teams enhance products with responsible, scalable AI for high leverage impact.

For accommodation requests related to employment information or application needs: all information provided will be kept confidential and used only as required to provide reasonable accommodations.

View full job page →

2d ago

Senior Lead Machine Learning Engineer

Capital One McLean, VA

McLean, VA

2d ago

$230k - $262k/yr Full time Artificial Intelligence Big Data Data & Ai

Apply Now

Job Description

Build and productionize large-scale reinforcement learning to power personalized customer experiences. Capital One is hiring a Senior Lead Machine Learning Engineer in McLean, VA (onsite) to design, build, deploy, and monitor recommender systems across Mobile, Web, and Email. The role combines ML engineering, cloud architecture, and operational excellence, with a team focused on reusable capabilities for enterprise experimentation.

What you’ll do

Design, build, and deliver machine learning models and components that address real business needs in collaboration with Product and Data Science
Shape ML infrastructure decisions based on modeling fundamentals, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Handle complex technical problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Partner within a cross-functional Agile team to create and improve software enabling state-of-the-art big data and ML applications
Retrain, maintain, and monitor models in production to keep systems effective over time
Use or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines that feed ML models
Apply CI/CD best practices, including test automation and monitoring, to support reliable deployment of ML models and application code
Ensure secure, well-managed code, strong governance from a risk perspective, and alignment with Responsible and Explainable AI best practices
Develop using Python, Scala, or Java

Team and impact

Within Card Tech, the Customer Intelligent Decisions & Experiences (CIDX) team is building the next generation of large-scale reinforcement learning-based recommender systems. These systems power personalized experiences across marketing, customer servicing, and digital products for millions of Card and MainStreet customers.

The team also contributes reusable capabilities to a Capital One-wide Experimentation Platform, helping users across the enterprise apply machine learning to a range of use cases.

Minimum qualifications

Bachelor’s Degree
8+ years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
4+ years of programming experience with Python, Scala, or Java
3+ years building, scaling, and optimizing ML systems
2+ years leading teams developing ML solutions

Preferred qualifications

Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
4+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
3+ years of experience developing performant, resilient, and maintainable code
3+ years of experience with data gathering and preparation for ML models
3+ years of people management experience
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
3+ years of experience building production-ready data pipelines that feed ML models
Ability to communicate complex technical concepts clearly to a variety of audiences
Experience leveraging interactive AI tooling to accelerate productivity, using capabilities beyond basic code completion

Tools you may use

Python, Scala, Java
AWS, Azure, Google Cloud Platform
scikit-learn, PyTorch, Dask, Spark, TensorFlow

Compensation and benefits

The annual salary range for this role is USD 229,900 - 262,400. This position is also eligible to earn performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

Capital One provides a comprehensive, competitive, and inclusive benefits package covering health, financial, and other support for your total well-being.

View full job page →

3d ago

Senior Machine Learning Engineer

Motion Recruitment Boston, MA

Boston, MA

3d ago

Full time Senior Agentic Ai Ai Agent

Apply Now

Job Description

A Senior Machine Learning Engineer role in Boston (hybrid) focused on designing scalable AI platforms and production-ready autonomous, agent-based systems.

Responsibilities

Highly hands-on engineering role driving day-to-day ML and AI development
Create, productionize, and maintain advanced AI and ML capabilities for enterprise research applications
Architect and improve distributed machine learning services and other scalable platform components
Build agentic applications, semantic retrieval pipelines, and orchestration frameworks that support them
Collaborate with software, platform, and security teams to deliver AI systems that are dependable, secure, and high-performing

Requirements

Deep knowledge of machine learning systems and autonomous agent design
Hands-on experience building LLM applications and retrieval-augmented generation (RAG) solutions
Practical experience working with vector search technologies and vector data stores
Familiarity with production ML operations, including monitoring and deployment practices
Strong Python development skills
Working knowledge of AWS infrastructure and Kubernetes environments

Key Technologies

Python
AWS
Kubernetes
Vector search technologies and vector data stores
LLM applications
Retrieval-augmented generation (RAG)
Large language model applications

Education

Bachelor’s degree or higher in computer science, engineering, data science, or a related discipline

Benefits

Comprehensive medical coverage
Dental insurance
Vision insurance
Paid vacation and personal time
401(k) retirement plan with employer match, where offered
Performance-based bonus eligibility

Preferred Background

Experience building software for legal technology, compliance, financial services, or another regulated domain
At least 12 years of software engineering experience, or 8 or more years combined with a PhD

Location & Work Authorization

Boston, MA (hybrid schedule)
Candidates must be authorized to work full-time in the United States both now and in the future

View full job page →

4d ago

Senior Machine Learning Engineer

Motion Recruitment Raleigh, NC

Raleigh, NC

4d ago

Full time Senior Agentic Ai Ai Agent

Apply Now

Job Description

Senior Machine Learning Engineer role focused on architecting scalable AI/ML and agentic workflows for enterprise data analytics, with an emphasis on legal-industry solutions.

Responsibilities

100% hands on delivery across the AI/ML lifecycle
Build and deploy advanced AI and machine learning models for legal research tools
Design and improve large-scale, distributed ML systems and enterprise platforms
Develop and integrate agent-based AI workflows and retrieval systems
Collaborate with engineering teams to help ensure SI solutions are scalable and secure

Requirements

Skilled in AI/ML and agentic workflows
Skilled in LLM and RAG architecture
Experience with vector databases
Exposure to MLOps
Exposure to Python
Exposure to AWS and Kubernetes

Desired Skills & Experience

Experience working in the legal industry
12+ years of development experience, or 8+ years of development experience plus a PhD
Degree in a relevant field

Technologies

Python
AWS
Kubernetes

Benefits

Medical Insurance
Dental Benefits
Vision Benefits
Paid Time Off (PTO)
401(k) (including match if applicable)

The Offer

Bonus OR Commission eligible

Location

Raleigh, NC (onsite)

View full job page →

4d ago

Senior Machine Learning Engineer

Motion Recruitment Boston, MA

Boston, MA

4d ago

Full time Senior Artificial Intelligence Automation

Apply Now

Job Description

This Senior Machine Learning Engineer role is focused on end-to-end ownership of machine learning for medical-device and robotics or automation applications, from sensor-driven data workflows through model deployment to constrained hardware.

Onsite Location

Boston, MA 02116 (onsite)

Role Summary

The position covers the full machine learning lifecycle for real-time device use cases. You will build and deploy ML models that translate sensor data into reliable outputs on constrained platforms. Collaboration with embedded, firmware, and software teams is a core part of the work, alongside contributions to MLOps and validation, including regulatory-ready documentation.

Responsibilities

Develop and troubleshoot workflows for collecting, cleaning, and organizing sensor data.
Build and refine ML models aimed at real-time device applications and performance improvements.
Partner with firmware teams to embed and test AI features on hardware platforms.
Set up and oversee tools for experiment tracking, automated evaluations, and deployment management.
Analyze model behavior, maintain reliability, and resolve issues to support high-quality outputs.

Technical Skills

Key technologies involved include Python, PyTorch, TensorFlow, and deployment toolchains such as TFLite, ONNX, CoreML, and TensorRT (or equivalent). The role also expects proficiency with MLOps, along with software engineering fundamentals supported by C and C++.

Requirements

Strong proficiency in Python.
Hands-on experience with PyTorch or TensorFlow.
Experience deploying models to edge using TFLite, ONNX, CoreML, TensorRT, or equivalent.
Experience building sensor data pipelines.
Proficiency with MLOps.
Solid software engineering fundamentals.
Proficiency in C or C++.

Desired Skills and Experience

Experience with physiological signal processing for medical or wearable applications.
Background in robotics or autonomous systems.
Experience working in a startup or small team.
Degree in a relevant field.