Top Machine Learning Engineer Jobs

Build and integrate ROS/ROS2 modules, collect and maintain ML training/test datasets from robots and mobile apps, implement end-to-end ML workflows, analyze data from internal databases, monitor data/model quality in production, and write maintainable Python/C++ code while collaborating across ML, engineering, product, and research teams.

Design, develop, and deploy AI/ML models and full-stack services (LLMs, RAG, vector search) for cybersecurity analyst workflows in secure cloud environments. Integrate systems via APIs, monitor model performance and drift, support retraining, and collaborate with stakeholders to deliver mission-focused solutions under DoD security constraints.

Design, implement, and maintain cloud-based AI infrastructure; build CI/CD pipelines and containerized deployments; manage AI model lifecycle, monitoring, scalability, and security; collaborate with AI/ML teams to optimize deployment and resource usage.

Lead design and implementation of agentic workflows, optimization algorithms, and ML/DL solutions for EDA. Research novel ML approaches, develop and maintain software (APIs, C/C++, Python) on Linux, and collaborate with global cross-functional teams. Requires ML/DL framework experience (PyTorch) and LLM/agentic AI understanding.

Develop, debug, and maintain AI/ML software solutions; collaborate with engineers and leadership to design systems; build tools for large-scale data analysis; produce documentation and user support materials.

Build and scale infrastructure and data pipelines for Safeguards ML research. Own training, evaluation, and scoring workflows, design researcher-facing tooling, ensure correctness and reliability, productionize high-value research workflows, and improve throughput, cost, and reliability of large-scale inference and scoring workloads while partnering with researchers.

Design, develop, test, and maintain LabVIEW-based and multi-language diagnostics and calibration software for reticle inspection platforms. Prototype and productionize AI/ML and generative-AI tools to improve diagnostics, calibration, and engineering workflows. Build CI/CD pipelines, participate in design/code reviews, troubleshoot system-level issues, and collaborate with cross-functional hardware, system, and manufacturing teams. Occasional travel (~10%).

Intern will build and operate training, data, and evaluation infrastructure: manage GPU clusters, orchestration, and large-scale data pipelines; instrument, monitor, and harden systems; test on robots; and deliver an end-to-end scoped project with mentorship. Opportunities to publish and attend conferences for qualifying interns.

Design, develop, and deploy advanced machine learning models for naval applications. Build cloud-native ML pipelines, apply distributed computing and MLOps/CI-CD practices, integrate models into operational DoD environments, and produce technical documentation per government standards. Collaborate with multidisciplinary teams and apply cybersecurity principles for secure shipboard and edge deployments.

Design, develop, and deploy machine learning models and data pipelines for naval applications. Build and optimize distributed, cloud-native analytics and event-streaming systems, work with multiple data formats and GIS tools, containerize and monitor deployments, apply DoD security practices, and collaborate with multidisciplinary teams to deliver mission-ready AI solutions in shipboard and operational Navy environments.

Build and operate Zoox's scalable ML training framework and platform to support distributed training, model lifecycle, validation, serving, and monitoring. Collaborate with ML researchers, software and data engineers to define requirements, design architecture, and reduce time from ideation to production for various ML use cases across the company.

As an ML Eval Engineer, you'll build evaluation systems for model quality, develop metrics to identify failures, and collaborate with teams to improve models using real-world datasets.

Build, configure, validate, and maintain AI/ML infrastructure and runtimes for internal developers. Support deployments, integrations, lightweight model fine-tuning, automation harnesses, and CI/CD/DevOps tooling while collaborating with stakeholders to prioritize work and educate users.

Lead design and productionization of scalable ML/LLM systems for legal products. Build LLM applications (RAG, agents), hybrid search, model serving, data pipelines, APIs, and cloud-native infrastructure. Optimize for latency, reliability, and cost; establish deployment/observability best practices; partner with data scientists and provide technical leadership and coaching.

Senior MLOps engineer to productionize NLP/GenAI models and RAG systems: build and automate ML pipelines, CI/CD, model registries, search/vector/graph-based retrieval, evaluation metrics and cost-optimized scalable infrastructure, collaborating with data scientists, product and operations teams.

The Senior Machine Learning Engineer will design evaluation frameworks, develop synthetic data pipelines, build evaluation systems, and collaborate with teams to improve model quality in healthcare AI.

The Senior ML Operations Engineer is responsible for building and maintaining ML infrastructure, model deployment processes, and collaboration with data teams to optimize predictive modeling solutions.

Design, develop, and maintain scalable ML and AI platform infrastructure for model training, deployment, feature engineering, candidate generation, agent deployment, and observability. Collaborate with data scientists and engineers, support platform operations, drive improvements, codify best practices, and influence platform investments to enable production ML systems at scale.

Responsible for designing and developing scalable data pipelines, storage systems, and monitoring tools for ML data infrastructure, ensuring system integrity and performance evaluation.

Design and develop data pipelines for autonomous driving systems, create storage for data metrics, build dashboards, and maintain data integrity, focusing on ML components.

Design, build, and operate ML-focused data infrastructure and pipelines that capture telemetry and model signals. Own, refactor, or replace systems for correctness, privacy, consistency, cost, and maintainability. Instrument new product surfaces, fix gaps, implement schema evolution and validation, and optimize storage/retention to support model and product teams.

Build distributed training, inference, and RL infrastructure; create libraries for large-scale data jobs; architect systems converting user data into training data; collaborate with researchers to accelerate iteration and reproducibility.

Build and improve large-scale GPU compute, storage, and software infrastructure to support training of agentic coding models. Work with ML researchers, cloud/OEM partners to design GPU clusters, improve training throughput, reliability, scheduling, data movement, automation, and developer experience across cloud and bare-metal environments.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.