Top AI & Machine Learning Jobs

Build AI agents that autonomously design, simulate, evaluate, and improve electrical and mechanical hardware from requirements through manufacturable outputs. Integrate CAD, EDA, and simulation tools; develop engineering representations, evaluations, datasets, feedback systems, and reproducible experiment infrastructure. Collaborate with electrical, mechanical, and manufacturing teams to ship agent-generated designs and measure improvements in design cycle time, hardware performance, and engineering effort.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Build and deploy AI agents within enterprise workflows by embedding with client teams, mapping processes, defining KPIs and evaluation plans, creating prototypes, and driving adoption. The role combines engineering, analysis, and executive-level communication. Responsibilities include training users, iterating on deployed agents, presenting findings to senior stakeholders, codifying successful approaches, and identifying additional AI transformation opportunities. The position is onsite in San Francisco with 20–40% client travel.

Support consulting engagements and research across the semiconductor and AI stack by performing technical analysis, cross-stack research, analytical modeling, and preparing client-facing reports, presentations, and briefings. Track industry trends, evaluate hardware and AI model developments, and collaborate with senior consultants to translate technical insights into strategic recommendations.

Develop AI communication systems for humanoid robots, including generative AI, NLP, speech-to-text, text-to-speech, computer vision, multimodal interaction, gesture recognition, and emotion-aware capabilities. Optimize transformer-based models for real-time edge execution and integrate speech, vision, and movement for natural human-robot interactions.

Research Engineer responsible for developing, training, evaluating, and deploying healthcare AI models and pipelines for conversational voice agents. The role focuses on instruction following, tool calling, retrieval, memory, speech and audio research, model optimization, benchmarking, failure analysis, and production integration. Candidates should have 5+ years of experience, including AI/ML engineering or research, production LLM deployment, Python proficiency, and a record of converting research prototypes into reliable measurable improvements.

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Build a personalized provider-recommendation pipeline using LLMs, semantic search, recommendation algorithms, agent workflows, and multi-source knowledge integration. Responsibilities include extracting user requirements, retrieving and ranking providers, connecting backstage decision pipelines, developing evaluation datasets, analyzing failures and feedback, and improving quality, reliability, and latency. The intern will collaborate with engineering and product teams from design through launch.

Build production AI-powered software for hiring, finance, capital planning, contract analysis, and operational workflows. Develop LLM features, MCP servers, and agentic systems; embed with finance, legal, recruiting, accounting, and operations teams; translate expert workflows into structured data and automated processes; and ship autonomous, user-friendly products rapidly.

Own backend architecture, code, deployments, monitoring, and on-call for AI shopping agents that perform live purchases. Build resilient checkout automation, scalable product ingestion infrastructure, embedding-based search and ranking, and systems that automatically detect and recover from failures. The role requires strong Python, end-to-end production ownership, and comfort with ambiguous, adversarial systems. Experience with AI agents, LLMs, machine learning, e-commerce, payments, or fraud infrastructure is preferred.