Top AI & Machine Learning Jobs

Lead and grow a data science team delivering production ML and analytics (forecasting, elasticity, classification), embed agentic workflows, ensure MLOps rigor (CI/CD, versioning, monitoring), package models as services, and link model outcomes to business metrics while partnering cross-functionally.

Build and scale an agentic AI stack for healthcare revenue cycle: implement voice AI pipelines, LLM orchestration, integrations with EHRs/RCM systems, real-time analytics, dashboards, and production-grade full stack features working closely with founders and customers.

Design and build data infrastructure and AI/ML systems—extracting, cleaning, moving, and storing data; develop, deploy, and monitor ML and LLM-based products; build APIs; collaborate with stakeholders and manage projects to meet business needs.

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.

Architect and implement robust bimanual manipulation algorithms and learning-based robot policies (diffusion, imitation, RL). Build end-to-end autonomy connecting perception to low-level control, write and maintain production Python/C++ code, and scale performance across a fleet in industrial environments.

Build and own backend services, APIs, and data pipelines powering an AI automation engine for enterprise hardware engineering. Implement scalable, secure infrastructure for high-volume LLM calls, design-tool integrations (PLM, CAD, CAE), document and design data ingestion, and work directly with founders and customers to rapidly prototype and ship production features.

Own and operate end-to-end systems: GPU inference for ASR, real-time low-latency transcription (WebSockets, failover), native apps (Swift, C#, Electron IPC), backend (Django, Celery, Postgres), infra (Fly.io, Terraform, AWS, Redis). Ship production-quality code across the full stack.

Build core infrastructure and ML models for a cloud-native IDE: architect the development environment, integrate with GitHub and Linear, fine-tune models, develop always-on AI agents, and design systems that continuously learn from user behavior.

Three-month remote training for Bay Area residents to gain hands-on experience with real ML projects: model building, data preprocessing, feature engineering, evaluation, and collaborative development using Pandas, NumPy, scikit-learn, and TensorFlow/PyTorch. Includes mentorship, portfolio projects, and a completion certificate.

Founding clinical AI researcher bridging medicine and engineering: define clinical requirements, curate and annotate datasets, develop and validate AI/NLP/LLM models for hospital quality and patient safety, engage hospital partners, design prompts and workflows, and produce clinical documentation and validation reports for product and regulatory use.