Top Machine Learning Engineer Jobs

The Machine Learning Engineer will design and implement data-driven machine learning solutions, collaborate with teams, and optimize scalable systems. Responsibilities include analyzing datasets, deploying analytical solutions, and leading teams.

Machine Learning Engineers design and implement data-driven solutions, build scalable ML systems, and collaborate with cross-functional teams at CapTech.

The Lead Machine Learning Engineer will design and implement scalable machine learning solutions, lead teams, and analyze large datasets using cloud technologies.

Lead Machine Learning Engineer is responsible for designing and implementing data-driven solutions, optimizing and deploying machine learning systems, and mentoring junior staff while working collaboratively across teams.

The Lead Machine Learning Engineer at CapTech designs and implements scalable machine learning systems, collaborates with teams, and provides technical leadership while optimizing enterprise data-driven solutions.

The Machine Learning Engineer designs and implements scalable solutions for clients, analyzing datasets, productionizing ML systems, and leading teams.

The Machine Learning Engineer designs and implements data-driven solutions, develops scalable ML systems, analyzes datasets, and leads client collaborations.

Develop and apply deep learning and data analysis techniques to create scalable vehicle simulation models, collaborating with teams to advance autonomous technology.

Lead quality engineering for PlayStation services by architecting scalable automated test frameworks (UI, API, integration, system), building CI/CD test pipelines, mentoring SDETs, defining test strategy and metrics, analyzing telemetry and production issues, and driving shift-left testing and quality improvements across teams.

The role involves building standardized training frameworks, profiling model performance, collaborating with teams, and improving related tooling.

Design, develop, deploy, and operate enterprise AI/ML solutions, including agentic systems, multimodal models, RAG, and responsible AI applications. Own MLOps and LLMOps practices, CI/CD, model versioning, observability, evaluation, monitoring, deployment, and lifecycle management. Lead architecture and technical decisions, partner with cross-functional stakeholders, resolve complex technical challenges, communicate strategy to leadership, and evaluate emerging AI technologies.

Design, train, and evaluate advanced machine learning models for autonomous driving, simulation, and ML platforms. Lead small cross-functional engineering teams, establish data and experiment practices for petabyte-scale training, address long-tail model failures, and work with multimodal datasets exceeding 100TB. The role requires commercial robotics experience, mentoring capability, expertise in VLA or generative world models, Python/PyTorch, sensor processing, and potentially edge deployment.

Own machine learning systems end to end, from data exploration and modeling through production deployment, monitoring, and iteration. Build scalable NLP, LLM, retrieval, ranking, recommendation, prediction, and agentic systems with explainable outputs. Partner with Product and Go-to-Market to deliver customer-facing AI capabilities, improve ML platform reliability, communicate technical work clearly, and mentor engineers.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

Own and improve the data infrastructure and core pipelines powering underwriting and claims. Build ingestion and enrichment pipelines for messy claims data, partner with data scientists and actuaries to productionize models and logic, and establish SLAs, monitoring, and on-call practices to ensure reliable, observable systems.

Design, train, and optimize high-performance ML/AI models and inference pipelines for remote sensor (OPIR) data. Integrate models into production Linux-based systems, collaborate with C++ and systems engineers, apply rigorous software engineering and Agile practices, and support missile warning, defense, and sensor-fusion applications.

Build and deploy ranking, matching, recommendation, and allocation ML systems for a global talent marketplace. Work across retrieval, scoring, inference (real-time and batch), feedback loops, and experimentation to optimize long-term hiring outcomes under marketplace constraints.

Build and operate large-scale data infrastructure for robot learning: ingest, validate, transform, and featurize multimodal data; run batch and streaming workflows; design storage and metadata systems; optimize training-time performance; manage data movement and lifecycle; implement observability and guardrails; collaborate with researchers and engineers.

Lead design, build, and productionize scalable ML systems on cloud platforms. Provide technical leadership, mentor teams, translate business needs into data-driven solutions, work with large datasets, implement recommender/NLP/vision models, leverage LLMs and agentic architectures, and support business development and client engagements.

Design, develop, and productionize scalable machine learning and data science solutions for clients. Analyze very large datasets across cloud platforms, implement recommender/NLP/computer vision and LLM-based systems, optimize for performance and scalability, leverage containerization and data warehousing, and lead/mentor junior practitioners while supporting business development and client strategy.

Lead design and delivery of end-to-end, production-scale ML systems on cloud platforms. Provide technical leadership, mentor teams, analyze and transform large datasets, implement recommender/NLP/risk models, productionize scalable ML, and grow practice through client-facing activities and business development.

Design, build, and productionize scalable machine learning systems; analyze and transform large datasets; implement recommender, NLP, and other analytic solutions; deploy models in cloud/enterprise environments; collaborate with clients and lead junior team members while contributing to business development.

The role involves designing and maintaining machine learning models, analyzing data, and optimizing algorithms to solve business problems.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Own and build the data foundation for an AI sports-performance platform: design data architecture, real-time streaming ingestion, time-series storage, feature store and millisecond feature serving, sport knowledge graph and evaluation/calibration frameworks, ensure tenant-level isolation and provenance, and work closely with domain scientists and AI engineers to deliver trustworthy, production-grade ML infrastructure.