Top Machine Learning Engineer Jobs

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

Own and improve Tessellate’s physician-to-organization matching engine by evolving rules-based matching toward semantic matching, embeddings, and vector search. Establish precision, recall, and evaluation practices; resolve healthcare organization identities across millions of records; and maintain a reliable, lightweight data platform. The role requires strong Python and SQL, applied machine learning expertise, production recommendation or ranking experience, and end-to-end ownership in an early-stage startup.

Develop and evaluate AI/ML-driven automation for ASIC and VLSI design and implementation. Explore GenAI, reinforcement learning, graph neural networks, and convolutional networks to optimize power, performance, area, and quality. Build prototypes, pilot automation on real SoC and IP designs, and collaborate with architecture, design, verification, implementation, academic, and industry teams. Contribute to CAD methodologies, tools, and digital transformation initiatives for advanced automotive processors and microcontrollers.

Design and build scalable data engineering and AI agent governance capabilities across major cloud AI platforms. Develop ingestion, processing, ETL, observability, security, lifecycle management, and identity governance features. Evaluate emerging AI agent frameworks and interoperability standards, troubleshoot customer issues, document architecture and processes, and collaborate with engineering, security, compliance, product, and support teams. The role also involves improving distributed data systems, mentoring engineers, and leading technical initiatives.

Design and deploy production-grade machine learning models and data pipelines across image, video, text, geospatial, time-series, and structured data. Collaborate with customers, data scientists, software and DevOps engineers to define requirements, maintain mission-critical deployments, and contribute to the Chariot AI operations platform.

Own and optimize infrastructure for large-scale ML training and inference. Build JAX-based pipelines, manage GPU and TPU workloads, improve distributed performance, and create systems for scheduling, monitoring, checkpointing, debugging, and reproducible experimentation. Partner with researchers to translate modeling ideas into production training runs and contribute to core training code supporting new architectures, modalities, and evaluation metrics.

Architect, train, and optimize multimodal deep learning models for autonomous vehicle perception. Build large-scale data mining, active learning, hard-example curation, and auto-labeling pipelines. Develop high-resolution vision and spatial-temporal transformer architectures, use foundation models for data curation and synthetic edge cases, and optimize models for onboard accelerator inference.

Develop, evaluate, and release learned driving policies for autonomous vehicles, particularly complex navigation and yielding scenarios. Bridge multimodal perception and planning outputs into safe behavioral actions, evaluate models in closed-loop simulation against safety metrics, and transition production-ready models into autonomous navigation systems. The role also advances data-driven policies, foundation-model experimentation, scenario generation, and end-to-end driving architectures.

Design and develop Persona's ML software stack for humanoid robots, applying ML to manipulation, navigation, locomotion, and perception. Build, test, and deploy models and data pipelines, monitor real-world performance, collaborate with partners and universities, and help grow the ML and autonomy teams.

Develop, fine-tune, and deploy LLMs for NLP tasks; design and maintain large-scale data pipelines; analyze time-series data; implement data-driven models and OCR systems; ensure data quality; collaborate cross-functionally; and research and apply best practices to improve internal data processing tools and infrastructure.

Build and scale ML development infrastructure and Agentic AI tooling. Optimize distributed training (PyTorch/DDP), improve GPU utilization, design multi-cloud Kubernetes environments, and collaborate with ML researchers and engineers to streamline the ML lifecycle.

Build and operate ML platform infrastructure supporting researchers and product engineers. Responsibilities include designing distributed systems, data pipelines, observability tools, scheduling and orchestration infrastructure, and GPU fleet systems. The role partners closely with ML research, owns reliability and performance, improves developer experience, and ships platform primitives in a high-ownership environment.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

Leads development and deployment of computer vision and AI/ML systems for real-time object tracking, monitoring, and intelligent data triage. Designs scalable architectures, agentic data-collection workflows, and production MLOps pipelines; mentors engineers, oversees data lifecycle and model deployment, and translates client needs into AI/ML roadmaps. Requires advanced Python, computer vision, Docker, Kubernetes, seven-plus years of machine learning experience, U.S. citizenship, and an active Secret clearance or higher.

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

Develop machine learning models to predict printability of complex CAD geometries using image, thermal, simulation, and sensor data. Design and maintain structured and unstructured data systems supporting diverse data ingestion, storage, and retrieval. Deploy models into production in collaboration with preprint, process, and applications teams. Experience with geometric data, production software engineering, and Python or C++ is required; powder bed fusion and manufacturing data workflows are preferred.

Lead technical direction and architecture for large-scale personalization and recommendation systems. Drive adoption of LLMs/foundation models, own retrieval-to-ranking pipelines, model lifecycle management, experimentation, and production scaling. Mentor teams and shepherd 0→1 AI platform initiatives into maintainable production.

Develop and deploy deep learning models for medical imaging: prototype and production work, design and run experiments, document results, collaborate on data labeling, and optimize models for inference and deployment.

Develop and productionize deep learning models for computer vision and NLP on medical imaging products. Design and run experiments, document results, collaborate on data labeling, write deployment/docker code, optimize inference speed, and conduct DL methodology research.

The Staff ML Engineer will define and build ML capabilities, focusing on production systems, strategy, and infrastructure in a cybersecurity context.

The Machine Learning Engineer will train, fine-tune, and deploy ML models for parsing unstructured data, and build data pipelines for product integration.

The AI/ML Engineer designs and operates a production-grade orchestration platform, focusing on multi-agent workflows, LLM integration, and backend systems. Responsibilities include managing cloud-native infrastructure, ensuring system reliability and observability.

The Staff AI/ML Engineer will develop and prototype AI/ML algorithms, analyze datasets, and mentor team members in an interdisciplinary environment.

Design and deploy object detection and tracking modules; develop ML algorithms; guide team members; improve data governance and quality assessment.