Top Machine Learning Engineer Jobs

Lead and manage a new ML Acceleration team, driving strategy and development for faster ML model training and optimizing performance.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead architecture and build cloud-native, resilient enterprise software and Agentic/Generative AI solutions for Claims Adjudication. Design microservices, APIs, CI/CD and observability; integrate LLMs, vector stores, and orchestration frameworks; ensure AI governance and production reliability while mentoring teams and aligning technical strategy with business outcomes.

Turn research prototypes into scalable, fault-tolerant ML and physics infrastructure for drug discovery. Improve GPU utilization, distributed execution, reproducibility, orchestration, monitoring, and packaging so scientists can run reliable, multi-cluster workflows and agent-usable pipelines.

Build and optimize edge and cloud data pipelines to run perception models, integrate foundation models for labeling and QA, scale data engine architecture, and develop MLOps tooling. Collaborate with DoD customers and use software-in-the-loop and hardware-in-the-loop testing to accelerate model iteration and deployment to edge systems.

Pioneering machine learning solutions for the defense sector, developing and deploying production-grade ML models, tackling complex data challenges, and collaborating with engineers.

Lead efforts in Object Detection and Tracking by architecting deep neural networks, adapting research papers into production solutions, and optimizing systems for real-time performance.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Seeking a Software Engineer specializing in AI/ML to design user-friendly interfaces, enhance security measures, and provide expert advice on AI/ML processes.

Lead design, training, and production deployment of state-of-the-art deep learning perception models (detection, segmentation, tracking, occupancy) for autonomous vehicles. Drive data strategy, corner-case mining, model productization, mentor engineers, and define roadmap for safe, production-grade perception systems.

Architect and manage ML pipelines for training and deployment on cloud platforms, automate CI/CD, and ensure system performance and availability.

Design and implement NLP and LLM-powered pipelines to extract, summarize, and semantically search technical documentation. Fine-tune transformer models, build RAG systems, convert unstructured documents into structured knowledge, optimize inference for production, and collaborate with engineers and SMEs to deploy document intelligence solutions.

Design, build, and operate an internal ML platform: CLI/SDK/frontend services, IaC integrations for compute/storage/serving, observability and cost tooling, model registry/feature store management, and developer-facing APIs to accelerate ML experiment-to-production workflows.

Design and build core components of the ML compiler, focusing on optimization strategies, compiler architecture, and collaboration with hardware teams.

As an ML Compiler Engineer, you will develop efficient programs for ML models, implementing transformations and optimizations within the compiler stack.

Design, build, and maintain scalable, secure ML pipelines and infrastructure for training, evaluation, deployment, and monitoring. Implement CI/CD for models, manage data pipelines, feature stores, and model versioning, and integrate security practices. Support deployment in on‑prem, cloud, hybrid, and classified environments and collaborate with researchers to productionize ML systems.

Research and build efficient ML systems for large-scale LLMs and agentic RL: design algorithms and system techniques, prototype in training/inference stacks, run large-scale experiments, and translate findings into production or publications.

As a Robotics ML Research Engineer, you will develop algorithms for robot manipulation, lead software infrastructure, and innovate in robotics learning research.

The Senior Simulation Engineer will develop core simulation technologies, integrate solutions, analyze results, and advocate for innovative methodologies in AD/ADAS systems.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

The role involves leading engineering efforts for ML infrastructure, optimizing distributed training, and enhancing AI platform efficiency in drug discovery.

As a Data Science and Machine Learning Software Engineer at NetApp, you will develop algorithms for real-time personalized search and recommendation using IoT data, while working on cloud-based services and analytics.

As a Lead ML Inference Engineer, you'll architect and develop a state-of-the-art inference platform, optimize performance, and ensure reliable ML serving for advertising applications.

Lead end-to-end ML system development: data exploration, model research, training, deployment, evaluation, and MLOps. Collaborate cross-functionally to prioritize claims, improve matching, measure business impact, and own a technical workstream.

Build and maintain backend services and ML infrastructure to integrate AI models into the Hume platform. Collaborate with research scientists and engineers to develop inference services, evaluation tools, cloud storage and compute solutions, and scalable deployment pipelines for production ML systems.