Top Machine Learning Engineer Jobs

Design, prototype, and productionize AI systems (recommendations, natural language interfaces, agents). Partner with product and engineering to identify AI opportunities, build scalable ML infrastructure, and manage the full ML lifecycle from data curation through training, deployment, and monitoring.

Build and productionize AI systems and infrastructure that power customer-facing features and internal tooling. Partner with product, design, and engineering to identify high-impact ML opportunities, prototype solutions (recommendations, NL interfaces, agents), and manage the full ML lifecycle from data curation to deployment and monitoring.

Design, prototype, and productionize AI systems and infrastructure—recommendations, natural language interfaces, agentic workflows—partnering with product and engineering to scale ML across the full lifecycle (data curation, training, deployment, monitoring).

Design, train, evaluate, and deploy production-quality machine learning models for conversational voice intelligence. Collaborate with researchers, engineers, and product teams to run experiments, improve model performance and reliability, operationalize ML systems, monitor production models, mentor teammates, and contribute to technical strategy and applied research.

Lead end-to-end design, development, and productionization of large-scale ML systems (recommendation, ranking, personalization). Architect scalable pipelines (data processing, training, deployment), build and optimize models (including LLMs), establish experimentation best practices, collaborate cross-functionally, and mentor engineers to drive responsible, impactful AI features.

Optimize and benchmark deep learning training and inference stacks on HPC/GPU hardware; profile and triage system bottlenecks; build automation tools; develop kernels and systems for new models; lead design reviews, review code, document work, and represent the lab at conferences.

Develop and implement large-scale machine learning and foundation-model systems, collaborate with research teams to integrate technologies, maintain ML lifecycle (data preprocessing, pre-/post-training, evaluation), conduct design reviews and code reviews, debug system issues, contribute to documentation and research publications, and represent the institute at conferences.

Lead technical strategy and build production-grade recommendation, ranking, and personalization ML systems. Design, deploy, and scale models and ML pipelines (Spark, Airflow), develop/fine-tune LLMs and AI Agents, run experimentation (A/B tests), mentor senior ICs, partner cross-functionally, and embed responsible AI practices.

Design, build, and operationalize scalable, secure Generative AI and LLM solutions across AWS and GCP. Lead architecture, implement RAG/vector search and agentic systems, optimize model lifecycle and compliance (HIPAA/Responsible AI), mentor engineers, and collaborate cross-functionally to deliver production-grade AI services.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current with ML/AI advances.

Design, implement, and optimize machine learning and deep learning models; preprocess and prepare large datasets; integrate models into production with software engineers; monitor and maintain production model performance; troubleshoot issues; and collaborate with stakeholders while staying current on ML/AI advances.

Design, train, and implement domain-specific LLMs for automation in restoration companies. Collaborate with teams, coordinate AI engineering, and ensure model reliability.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

The role involves enhancing a search-based question-answering platform through ranking features, algorithm frameworks, and performance metrics while collaborating with various teams to solve engineering challenges.

Build, optimize, and scale end-to-end ML infrastructure for training, evaluating, and serving large language models. Implement distributed training and low-latency inference pipelines, abstractions to automate ML workflows, collaborate cross-functionally, and drive best practices for ML and data engineering to support production LLMs at scale.

Lead technical development of machine learning algorithms for the onboard Planner, advancing imitation learning, reinforcement learning, and model scaling; integrate foundation models; create metrics, data, and training frameworks; mentor ML developers; and collaborate across perception, simulation, system design, and safety teams to solve autonomous driving challenges in complex urban environments.

Develop and optimize a high-speed GPU-based simulation framework for ML training and validation. Apply reinforcement learning to solve behavior and path-planning challenges, bridge sim-to-sim and sim-to-real fidelity gaps, enable self-serve data generation for autonomy users, and write production-ready code integrating ML into core simulation architecture.

Develop machine learning algorithms for robotic welding perception, maintain data pipelines, evaluate models, and integrate them into production services.

The Data Scientist/ML Engineer designs and deploys Machine Learning algorithms, assists federal customers, and contributes to new feature design.

The role involves training and deploying deep learning models for semantic reasoning in autonomous vehicles, focusing on highway environments and optimizing systems for real-time performance.

Lead design, training, and deployment of custom deep-learning models for Radar and EO/IR semantic segmentation, detection, and temporal tracking. Build multi-stage pipelines, train on large proprietary datasets, optimize C++ inference for resource-constrained ARM64/CPU edges, advance sensor-fusion with AIS and cartography, integrate ML outputs into multi-target tracking/SLAM, and contribute to MLOps for continuous training and evaluation.

Design, build, and deploy production-grade AI/ML systems for Defense and Intelligence clients. Architect scalable, mission-oriented ML solutions; implement and operationalize models across modalities; work on deep learning, computer vision, NLP, and signal processing; integrate deployments into varied mission environments and support MLOps and software deployment.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

Develop and deploy 2D traffic sign detection models for autonomous driving. Responsibilities include scenario analysis, dataset curation, model training and evaluation, optimization and quantization, building evaluation pipelines, long-tail data mining, and validating performance on edge/onboard platforms while collaborating across data, infrastructure, and deployment teams.

Develop, optimize, and deploy machine learning models and pipelines for scalability. Build ML platform tools (Kubernetes, Helm, EKS), manage large datasets, design ETL/ELT processes, implement predictive modeling with gRPC, adapt research to production, and collaborate with cross-functional teams and customer success.