Machine Learning Pipelines

Machine Learning Pipelines Engineer Jobs

91 jobs found

Today

Sr Machine Learning Engineer

Walt Disney Attractions Technology LLC Orlando, FL

Orlando, FL

Today

$137k - $184k/yr Full time Ai Ml Artificial Intelligence Azure Machine Learning

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Job Description

Walt Disney Attractions Technology LLC is building an enterprise AI enablement platform for Disney Experiences Technology, aimed at providing streamlined AI and Generative AI capabilities across the DX segment. In this onsite role in Orlando, FL, you will design, develop, and deploy high-impact AI/ML solutions, including agentic systems, multi-modal models, and Responsible AI applications, while taking end-to-end ownership of the AI/ML lifecycle.

About the role

The DXT AI Technology Platform team focuses on creating a platform that lets teams build solutions around and on top of robust AI and Generative AI capabilities. The Senior Machine Learning Engineer will own implementation through production operations, covering LLMOps/MLOps, evaluation, CI/CD, observability, and responsible deployment practices.

Responsibilities

Own the operational backbone of the AI/ML platform by designing and running CI/CD pipelines, managing model and agent versioning, automated deployment, rollback, and release management.
Build comprehensive observability and monitoring for model and agent performance, drift, data quality, latency, cost, and reliability, including alerting and automated remediation where possible.
Develop production-scale AI systems, including multi-step agentic workflows and multi-agent orchestration platforms, and operationalize them end to end.
Drive complete ownership of the AI/ML lifecycle, covering implementation, testing, deployment, and continuous operational monitoring to deliver projects on schedule and to specification.
Design and implement Responsible AI frameworks, including hallucination detection, safety guardrails, evaluation systems, and observability to support reliable, accurate, and ethical deployment.
Establish evaluation frameworks for Large Language Models and agent-based systems, including measures for model quality, task success rates, safety compliance, and operational effectiveness.
Champion LLMOps and MLOps best practices, including infrastructure-as-code, reproducibility, automated testing, and environment parity across the platform.
Partner with cross-functional stakeholders including product managers, data scientists, application teams, vendors, and partners to align on requirements and iterate toward successful outcomes.
Provide hands-on technical leadership by driving architectural decisions across AI development, LLMOps, quality assurance, and production deployment.
Proactively identify and resolve technical blockers that could affect project timelines or deliverables.
Communicate technical strategy and progress to executive leadership and key stakeholders with clarity and confidence.
Engage directly in development and problem-solving on high-complexity technical challenges to maintain project velocity and quality.
Drive innovation through research and experimentation with emerging AI technologies and frameworks, evaluating and integrating capabilities that strengthen the platform.

Requirements

5+ years of proven expertise designing, building, and deploying AI/ML solutions at scale, including 1–2 years of production experience in Generative AI technologies.
Comprehensive MLOps/LLMOps experience with hands-on implementation of CI/CD pipelines, model and agent monitoring, versioning, observability, and lifecycle management in production.
Production deployment experience on AWS, Azure, or GCP, with demonstrated ability to architect, scale, and operate cloud-native ML solutions.
Expert-level programming proficiency in Python and AI/ML development ecosystems.
Strong foundation in machine learning, including statistical modeling and supervised and unsupervised learning algorithms.
Advanced prompt engineering skills with a deep understanding of optimization techniques and best practices for LLM interactions.
Versatile ML background spanning traditional techniques (classification, regression, clustering) and deep learning approaches.
Production-grade Generative AI experience deploying and maintaining LLMs and multi-modal models in live environments.
Exceptional analytical capabilities with a record of solving complex technical problems in ambiguous, fast-changing contexts.
Outstanding communication and collaboration skills to translate complex technical concepts for diverse audiences and drive cross-functional alignment.
Success partnering across organizational levels, from individual contributors to senior leadership.
Proven ability to influence and lead in matrix organizations through collaboration and relationship-building.

Technologies

Python
AWS, Azure, GCP
CI/CD
LLMOps, MLOps
Infrastructure-as-code
Docker, Kubernetes, Terraform

Location and compensation

Location: Orlando, FL (onsite)
Salary: USD 137,200 - 184,000 per yearly

Education

Bachelor's degree in Computer Science, Machine Learning, Mathematical Sciences, Information Systems, Software, Electrical or Electronics Engineering, or a comparable field, and/or equivalent work experience.
Preferred: Master's degree or Ph.D in Artificial Intelligence, Machine Learning, Mathematical Sciences, Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or a comparable field, and/or equivalent work experience.

Preferred qualifications

Experience with container orchestration and infrastructure-as-code (Docker, Kubernetes, Terraform) for ML workloads.
Experience with vector databases and embedding technologies.

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Today

Lead Machine Learning Engineer - News

Disney Entertainment & Sports LLC Glendale, CA

Glendale, CA

Today

$142k - $190k/yr Full time Ai Ml Artificial Intelligence Aws Data Engineering

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Job Description

Disney Entertainment & ESPN Technology is seeking a Lead Machine Learning Engineer to strengthen the News ML team in Glendale, CA. This onsite role focuses on shaping the technical direction and delivering scalable, end-to-end machine learning systems that support personalized real-time news experiences across web, mobile, and connected TV platforms.

In this position, you will take ownership of complex technical initiatives from technical design through production deployment, with an emphasis on operational excellence. You will build the infrastructure and workflows that power the full machine learning lifecycle, while partnering closely with product, editorial, and engineering stakeholders to translate business needs into reliable technical solutions.

What You’ll Do

Lead complex technical initiatives end-to-end, covering technical design, production deployment, and operational excellence
Design and develop machine learning infrastructure, including data pipelines and workflow orchestration, data discovery and quality tools, and feature libraries
Deliver data and ML-driven solutions across engineering use cases such as recommendation systems, object detection, autogenerated tagging solutions, and RAGs
Collaborate with product, editorial, and engineering stakeholders to convert business requirements into robust technical implementations
Set priorities across initiatives and technical workstreams to achieve high-impact, time-sensitive outcomes while identifying, communicating, and mitigating risks
Promote engineering best practices across code quality, testing, CI/CD, observability, and incident response
Mentor and coach engineers to support ownership, collaboration, and continuous improvement
Contribute to technical documentation and support knowledge sharing across teams

Required Qualifications

Bachelor’s degree in computer science, Information Systems, Statistics, Math, or a comparable field of study, and/or equivalent work experience
7+ years of software engineering experience
5+ years of hands-on experience developing and deploying machine learning systems in production
Expertise in data science, deep learning algorithms, or statistical methods for real-world engineering problems
Comfort across the predictive stack, including data collection, data analysis, feature engineering, batch training, and low-latency online serving
Experience designing and developing backend microservices for large-scale distributed systems using REST
Cloud infrastructure experience, preferably AWS, including Step Functions, Lambda, Glue, SQS, SNS, and Personalize
Familiarity with developing and deploying Spark and ML pipelines
Hands-on experience with big data technologies such as Databricks, Kinesis, and Kafka
Proven leadership, coaching, and mentoring skills with the ability to inspire and empower a team toward business goals
Experience with observability tools for metrics, logging, and monitoring such as Datadog
Experience working in Agile/Scrum development environments
Excellent communication skills and a commitment to collaboration in a fast-paced, guest-focused environment

Role Details

Location: Glendale, CA (onsite)
Compensation: USD 141,900 - 190,300 per year
Experience level: 7+ years

Technology Stack

AWS, Step Functions, Lambda, Glue, SQS, SNS, Personalize
REST, Spark, Databricks
Kinesis, Kafka
Datadog

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3d ago

Machine Learning Engineer (TS/SCI)

Vantor Services Inc. Herndon, VA

Herndon, VA

3d ago

$165k - $215k/yr Full time Ai/ml Data Engineering Artificial Intelligence Computer Vision Ml

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Job Description

Vantor Services Inc. is seeking a Machine Learning Engineer to help design and deploy computer vision and machine learning systems for GEOINT-focused missions. The role is onsite in Herndon, VA and requires an active U.S. Government TS/SCI clearance.

This position centers on building CV/ML pipeline components, enabling synthetic data generation using generative AI for GEOINT data, and supporting production machine learning and data engineering.

Role Summary

Design, develop, and deploy multiple computer vision (CV) pipeline components
Implement synthetic data generation using generative AI for commercial and government GEOINT data
Provide production ML and data engineering capabilities across the AI/ML workflow

Responsibilities

Design, develop, and deploy multiple CV pipeline components, including items such as image preprocessing, distributed training, inferencing engine, and data store
Implement synthetic data generation capabilities using generative AI applied to commercial and government GEOINT data
Perform data engineering to transform various data types to support phases of the AI/ML workflow
Stay current on CV technical advances from industry and academia, and apply them to customer mission problems
Brief internal Vantor teams on delivery progress, evaluation results, and project challenges

Required Qualifications

U.S. citizenship and an active TS/SCI clearance (with willingness to get a CI Polygraph)
5+ years of programming experience using at least one general purpose programming language: Python, Typescript, Java, or C/C++
2+ years leading design or architecture of new and existing systems, including design patterns, reliability, and scaling
2+ years of relevant experience in machine learning engineering, data engineering, backend software engineering, or a closely related technical role
Hands-on experience training, fine-tuning, or adapting machine learning and AI models using a modern ML framework
Experience deploying machine learning models into production, including building batch or real-time inference pipelines and managing model versions
Hands-on experience building and operating services on a major cloud platform, including managed compute, databases, storage, and identity and access management
Experience building and operating workflow orchestration using Kubernetes for data or ML pipelines
Experience owning containers, container orchestration, CI/CD pipelines, and infrastructure-as-code
Proven ability to support production systems, including debugging, observability, secure configuration, and incident response
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, Machine Learning, or a related discipline, or equivalent demonstrated experience

Technologies

Python, Typescript, Java, C/C++, generative AI
Modern ML framework, Kubernetes, CI/CD, infrastructure-as-code
Cloud platform components including managed compute, databases, storage, identity and access management
LLMs, AI agents

Preferred Qualifications

Hands-on experience with LLMs, AI agents, or agentic workflows
Experience working with satellite or aerial imagery, including raster formats such as GeoTIFF, coordinate reference systems, and tools such as GDAL, Rasterio, Shapely, Fiona, and GeoPandas
Experience developing computer vision models for tasks such as object detection, semantic or instance segmentation, or change detection, ideally applied to overhead imagery

Clearance Requirement

This position requires an active U.S. Government security clearance at the TS/SCI level.

Compensation

USD 165,000 - 215,000 per year
Base pay transparency: $165,000.00 - $215,000.00 (including California, Colorado, Hawaii, New Jersey, the Washington, DC metropolitan area, and all other states)

Benefits

Robust 401(k) with company match
Mental health resources
Student loan repayment assistance
Adoption reimbursement
Pet insurance

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3d ago

Machine Learning Engineer, Enterprise Brain

Glean Technologies Mountain View, CA

Mountain View, CA

3d ago

$200k - $280k/yr Full time Ai Ml Artificial Intelligence Data Architecture

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Job Description

Build proactive enterprise AI with Glean’s Enterprise Brain team, helping detect opportunities and automate tasks for users through the Enterprise graph and advanced ML and LLM techniques. This role blends rigorous ML engineering with quality-focused delivery so you can ship evaluated, production-ready solutions that improve understanding, prediction, and agentic system performance.

Location: Mountain View, CA (hybrid, 4 days a week in our Mountain View office)
Compensation: USD 200,000 - 280,000 per year

What you’ll do

Work on difficult ML challenges tied to user understanding and task prediction.
Develop new LLM workflows and signals to improve reasoning, planning, and personalization.
Design and optimize reinforcement learning and fine-tuning approaches to raise the quality of understanding, prediction, and other agentic systems.
Lead development of scalable evaluation, benchmarking, and optimization loops.
Build and maintain robust ML pipelines for enterprise and knowledge graph construction.
Drive initiatives to measure, monitor, and improve data quality, model quality, and end-to-end system performance.
Collaborate with cross-functional teams to understand customer pain points and deliver high-quality, production-ready ML solutions.
Mentor junior engineers, or learn from experienced teammates, in a tight-knit, high-velocity environment.

What you bring

6+ years of industry experience in machine learning engineering.
A BA/BS in computer science, math, sciences, or a related field.
Proven ability to design, build, and ship production-ready models and systems.
Demonstrated expertise in ML evaluation, benchmarking, and data quality.
Proficiency in an ML framework of your choice, such as TensorFlow or PyTorch.
Comfort working in a fast-moving, ambiguous environment by turning open-ended problems into clear priorities and production-ready outputs.
A practical approach to balancing speed and quality, making tradeoffs that support measurable progress and production readiness.

Tools and frameworks

TensorFlow
PyTorch

Benefits

Comprehensive benefits package including competitive compensation
Medical coverage
Vision coverage
Dental coverage
Generous time-off policy
Opportunity to contribute to your 401k plan
Home office improvement stipend
Annual education and wellness stipends
Regular events
Healthy lunches daily

Additional details

Compensation & benefits note: The standard base salary range for this position is $200,000 - $280,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.

AI-first mindset at Glean: As part of the interview process, you’ll complete a brief AI-focused exercise or discussion to understand how you think about, design, and use AI to drive impact in your role. You can reference tools, platforms, or workflows you use today; prior Glean experience isn’t required.

Global data privacy notice for job candidates and applicants: Depending on your location, GDPR, CCPA, or other privacy laws may regulate how we manage the data of job applicants. Our full notice is available in our Privacy Policy. By submitting your application, you agree to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement.

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3d ago

Associate Machine Learning Engineer, Recommendations

TikTok USDS JV San Jose, CA

San Jose, CA

3d ago

$123k - $259k/yr Full time Ai Ml Ai/ml Data Engineering Artificial Intelligence

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Job Description

In this role at TikTok USDS JV, you will help build and operate the machine learning infrastructure behind hyper-scale feed and e-commerce recommendation systems. The position centers on reliable MLOps and data pipelines for real-time streaming, feature stores, model evaluation, and large-scale deep learning deployment with monitoring.

Working onsite in San Jose, you will collaborate with algorithm researchers and platform teams to move recommendation prototypes into zero-downtime production, while improving candidate generation and ranking for CTR and CVR serving.

What you’ll do

Design, build, and maintain robust MLOps pipelines (including CI/CD for ML) for hyper-scale recommendation models supporting 10M–100M+ users, with continuous training, automated evaluation, and seamless deployment.
Architect high-throughput real-time streaming data pipelines and feature stores, including offline and online feature synchronization to model dynamic user interest using short-term and long-term signals.
Optimize production predictive models for candidate generation and ranking, focused on CTR and CVR.
Improve inference performance through acceleration, model quantization, parallel computation, and latency reduction using C++/CUDA/TensorRT or comparable frameworks.
Build automated tools for monitoring, debugging, and visualization to track feature drift, model performance degradation, data leakage, and GPU/CPU cluster utilization.
Partner with algorithm researchers and platform teams to streamline the lifecycle of large-scale deep learning models, from experimental prototypes to zero-downtime production deployment.

Required qualifications

Bachelor’s degree or higher in Computer Science or a related field.
Strong programming and problem-solving ability.
Basic understanding of MLOps principles, including data preprocessing, containerization (Docker), model tracking/versioning, or workflow orchestration.
Motivation to address complex real-world distributed systems and machine learning operational challenges.

Technologies you’ll work with

MLOps, CI/CD
Docker
C++, CUDA, TensorRT
CTR, CVR

Compensation

USD 122,574 - 259,200 per year

Benefits

Day one access to medical, dental, and vision insurance
401(k) savings plan with company match
Paid parental leave
Short-term and long-term disability coverage
Life insurance
Wellbeing benefits
10 paid holidays per year
10 paid sick days per year
17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
Eligible for additional discretionary bonuses/incentives and restricted stock units

Team and company context

You will join a group of applied machine learning engineers and data scientists focused on general feed recommendations and E-commerce recommendations. The team develops innovative algorithms and techniques to improve user engagement and satisfaction, turning creative ideas into solutions with business impact. They apply large-scale machine learning to solve real-world problems.

TikTok USDS Joint Venture LLC is dedicated to the safety and security of millions of Americans who create, discover, and connect with what they love on the apps operated by the company. The Joint Venture is established in compliance with an Executive Order signed by President Trump on September 25, 2025, and operates a comprehensive data privacy and cybersecurity program under defined safeguards to protect national security and secure U.S. user data, apps, and the algorithm. The company is shifting from a hybrid work model to a fully in-person schedule up to 5 days a week.

Preferred qualifications

Experience in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.

Reasonable accommodation

USDS is committed to providing reasonable accommodations in recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other reasons protected by applicable laws. To request assistance, contact: https://tinyurl.com/USDS-RA.

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6d ago

Machine Learning Engineer

Engramme San Francisco, CA

San Francisco, CA

6d ago

Full time Python Artificial Intelligence Data Analysis

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Job Description

Engramme is hiring a Machine Learning Engineer in San Francisco to build and run production-grade ML systems for memory retrieval and contextual understanding.

Responsibilities

Design, implement, and iterate on ML models for memory retrieval and ranking
Develop ML infrastructure and training pipelines that support scalable experimentation
Deploy models into production and monitor performance at scale
Optimize model performance and latency for real-time use cases
Work with vector databases and embedding systems
Implement MLOps best practices, including monitoring and operational workflows
Partner with the research team to productionize new algorithms and improve reliability

Requirements

4+ years of ML engineering experience
Strong proficiency in Python and ML frameworks including PyTorch and TensorFlow
Experience deploying ML models to production
Knowledge of MLOps, model serving, and monitoring
Understanding of NLP, embeddings, and retrieval systems
Experience with AWS and/or GCP and containers
Strong software engineering fundamentals
Experience with large-scale data processing

Technologies

Python
PyTorch
TensorFlow
AWS
GCP
Containers
Vector databases
Embedding systems

Nice to Have

Experience with large language models and prompt engineering
Knowledge of vector databases such as Pinecone, Weaviate, or Milvus
Experience with recommendation systems or search
Background in information retrieval or ranking systems
Knowledge of model optimization and quantization
Experience with real-time ML systems
Familiarity with transformer architectures

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6d ago

Principal Machine Learning Engineer

Cushman & Wakefield Creve Coeur, MO

Creve Coeur, MO

6d ago

$179k - $210k/yr Full time Ai Ml Artificial Intelligence Azure DevOps

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Job Description

Principal Machine Learning Engineer role for onsite work in Creve Coeur, MO, focused on end-to-end AI/ML architecture, MLOps/LLMOps, and production monitoring.

Build and maintain AI/ML systems across the full lifecycle: end-to-end architecture, development, deployment, and ongoing optimization
Shape architecture for AI use cases including machine learning, GenAI, and agentic models tailored to business requirements
Design agentic AI systems with multi-agent orchestration, tool-use, evaluation, and autonomous task-execution pipelines
Set and enforce ML development standards covering data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation
Partner with data engineering to deliver robust, scalable, high-quality data pipelines for both training and inference
Implement MLOps and LLMOps including CI/CD pipelines for data wrangling, feature engineering, model training, evaluation, deployment, and monitoring
Define model operations standards for versioning, experiment tracking, reproducibility, and model registry management
Operate production monitoring to detect performance degradation, data drift, concept drift, and bias, including automated alerting and retraining triggers
Support AI governance with policies for model risk management, fairness, explainability, privacy, and security
Embed human oversight and accountability across the AI development lifecycle

Requirements

10+ years building software or machine learning systems, including 6+ years shipping and operating ML in production (served, monitored, and maintained after launch)
Experience acting as the senior-most or near-senior-most engineer on a production ML or data platform, accountable for architectural decisions
Track record of establishing technical standards that continue beyond your involvement
Ability to lead through influence without formal authority
Hands-on Python and SQL plus software engineering fundamentals: version control, automated testing, code review, modular design, and enforcement across teams
Expert hands-on proficiency with Databricks and the Azure data ecosystem, including: Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, and Model Serving
Experience with MLflow (or equivalent) for experiment tracking, model registry, tracing, and reproducibility
At least 1 year building agentic systems that reached production use (or equivalent demonstrated work)
Proven ability to mentor engineers at every level and raise team technical standards
Familiarity with modern machine learning frameworks and reusable, scalable engineering patterns

Technologies

Python, SQL
Databricks (Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, Model Serving)
Azure data ecosystem
MLflow
CI/CD
REST endpoints
Azure DevOps
Databricks Declarative Automation Bundles (DABs)
GitHub Actions
Terraform
Model Context Protocol (MCP)

Benefits

Health, vision, and dental insurance
Flexible spending accounts
Health savings accounts
Retirement savings plans
Life and disability insurance programs
Paid and unpaid time away from work
Competitive pay (varies by eligibility factors such as geographic location, date of hire, total hours worked, job type, business line, and applicability of collective bargaining agreements)
The company will not pay less than minimum wage for this role

Preferred Skills, Knowledge & Experience

Experience establishing an ML/AI platform capability in an organization that did not previously have one
Experience embedding across multiple teams, geographies, and time zones as a principal-level contributor
Familiarity with CI/CD and infrastructure-as-code tooling for data pipelines using Azure DevOps, Databricks Declarative Automation Bundles (DABs), GitHub Actions, Terraform, or equivalent
Good understanding of data governance, metadata management, and cataloguing in enterprise environments
Exposure to model serving and inference at scale (containers, REST endpoints, latency and throughput targets) or feature stores, retrieval pipelines, or LLM cost-attribution tooling
Model Context Protocol (MCP) or comparable tool-integration standards
Domain exposure such as commercial real estate or professional services where data is messy, entities are inconsistently defined, and subject matter experts are the source of truth