Top AI & Machine Learning Jobs

Develop and integrate deep-learning computer vision and perception algorithms for autonomous vehicles. Responsibilities include managing data pipelines, designing and optimizing neural networks, defining performance metrics, leading long-term projects, communicating results, and aligning algorithms with product needs. The role requires strong computer vision, deep learning, mathematics, Python, and PyTorch expertise, with C++, Linux, Git, and research publications advantageous.

Develop and productionize AI/ML solutions for healthcare, focusing on multimodal LLMs, conversational AI, document understanding, search, and Q&A. Responsibilities include evaluating and fine-tuning models, building training data pipelines, managing the full ML lifecycle, deploying scalable solutions, troubleshooting integrations, documenting architecture, and ensuring HIPAA compliance. The role requires strong Python and PyTorch skills, experience with LLM training platforms and cloud ML services, and close collaboration in a fast-paced startup environment.

Build and deploy AI agents, analytical tools, and automated workflows for strategy, deal sourcing, market intelligence, CRM operations, due diligence, and executive decision-making. Collaborate with the CSO and cross-functional teams while contributing to Walden’s shared agent platform. Own agent feedback loops, evaluations, observability, optimization, and reusable infrastructure using modern LLM tools, Python, data engineering, APIs, and workflow automation.

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.