Top Machine Learning Engineer Jobs

Lead and own the ML/AI function: architect multi-modal agentic systems, define evaluation frameworks and datasets, productionize reliable scalable ML, ensure compliance, and build and mentor a high-performing ML team.

Build and scale GPU compute infrastructure, training frameworks, and data pipelines. Integrate large-scale training and inference systems, productionize models with ML teams, ensure scalability and reliability, troubleshoot full-stack ML systems, and mentor junior engineers.

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.

The Data/ML Infrastructure Engineer builds and operates data infrastructures for processing drone and orbital sensing data, ensuring reliability, observability, and performance while collaborating with research and product teams.

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 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.

Design, implement, and evaluate perception algorithms and 4D world representations for autonomous vehicles. Train and deploy ML and computer vision models from data collection to production, test on real vehicles, and collaborate with data, behavior, and research teams to deliver production-ready autonomy software.

Build tools and infrastructure to enable large-scale end-to-end autonomy research and productionize self-driving software. Scale training, manage large datasets, identify and remove bottlenecks across GPU compute, data curation, and evaluation systems while collaborating with AI research and engineering teams.

Optimize and deploy ML models to production embedded runtimes. Improve inference latency and efficiency across accelerators, prune/quantize models for memory‑constrained platforms, profile on target hardware, and collaborate with ML and software teams on architecture and integration.

Build and operate end-to-end ML infrastructure: distributed cloud GPU training, datasets, training frameworks, evaluation, deployment, and integrate pipelines into product workflows while collaborating with modeling teams.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

Develop the next generation AI compiler for Mythic's analog compute, optimizing for dataflow architectures and collaborating with hardware engineers.

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, 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.

Design, implement, and own end-to-end scalable ML systems and MLOps lifecycle (deployment, monitoring, metrics). Optimize models and system performance, work with Java/Python and TF/PyTorch, use CI/CD, Terraform, Docker/Kubernetes, lead inter-team communication, document work, and meet deadlines.

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.

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.

Lead integration strategy and execution for Zoox's agentic platform. Collaborate with teams on tool integration, debug failures, and ensure resource readiness for production. Define integration artifacts and contribute to release processes.

Build production decision systems that estimate causal effects and uncertainty, learn from interventions, optimize constrained policies (pricing, inventory, promotions), evaluate counterfactuals and challengers, and deploy safe, automated ML-driven business decisions to generate measurable economic lift.

Develop and support Rescale's HPC platform, integrate AI into CAE workflows, solve customer HPC/AI problems, build AI software, collaborate with engineering teams, and engage directly with customers.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.