Top AI & Machine Learning Jobs

Build and ship AI-powered internal systems across investing, talent, founder services, finance, and operations. Projects include talent intelligence, sourcing and research tools, diligence workflows, firm knowledge search, operations automation, and reusable portfolio-company AI tools. The role requires end-to-end ownership, from identifying high-leverage problems and selecting the technical stack to building usable products, working with messy business data, and supporting portfolio founders with AI strategy and implementation.

Own the architecture and technical direction of a production perception system for home security hardware. Build robust computer vision and machine learning pipelines for multi-camera tracking, sensor fusion, inference, and edge deployment. Improve accuracy, latency, reliability, observability, and graceful failure handling across challenging environmental conditions. Lead evaluation infrastructure, field-data feedback loops, system interfaces, technical reviews, and cross-functional collaboration with hardware, cloud, and product teams.

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

Build production-grade AI and agentic systems for healthcare workflows. Responsibilities include designing data pipelines, evaluation harnesses, model deployment and monitoring systems, and scalable LLM- and vision-based applications. The role spans backend and full-stack development, AI agent orchestration, experimentation, training pipelines, and frontend integration while collaborating with ML researchers and engineering teams.

Leads Elastic’s agentic engineering platform for professional services. Designs and deploys production LLM systems using orchestration, tool integration, RAG, structured outputs, evaluation frameworks, tracing, monitoring, and human approval workflows. Owns architecture, technical standards, roadmap, quality, security, and lifecycle optimization while collaborating with services teams. The role emphasizes production reliability, sensitive-data protection, and client-ready outputs, with valuable experience in SIEM, observability, data migration, and Elasticsearch.

Build and productionize computer vision, 3D perception, and LLM-powered systems using proprietary LiDAR and imagery datasets. Own AI models throughout data preparation, training, evaluation, deployment, and monitoring. Develop agentic workflows, MCP integrations, and AI copilots while creating metrics tied to customer outcomes. Collaborate across hardware, software, and customer teams to rapidly prototype and deliver reliable infrastructure intelligence products.

Own enterprise LLM post-training from data curation through production deployment. Responsibilities include fine-tuning open-weight and proprietary models, building evaluation suites and benchmarks, calibrating LLM judges, managing regression risks, operating data flywheels, optimizing cost and privacy, and working directly with enterprise stakeholders. Success is measured by reliable production impact and shipped models.

Build and ship production-grade AI features by embedding with clinical, product, and operations teams. Own solutions end to end, including architecture, evaluation, validation, governance, deployment, monitoring, and human-in-the-loop safeguards. Develop agentic capabilities using tool calling, RAG, MCP integrations, and orchestration on AWS Bedrock. Establish acceptance criteria and risk controls, contribute reusable platform assets, and improve AI fluency across teams. The role requires strong Python, cloud engineering, full-stack development, and experience delivering validated AI features in production.

Build and deploy production-grade AI agents, workflow automations, and internal applications that improve enterprise processes. Partner with business, operations, engineering, and security teams to identify use cases, design reliable agent architectures, prototype with LLMs and agent frameworks, measure impact, and drive adoption. The role requires strong systems engineering, API, data, and workflow design skills, along with the ability to translate ambiguous business problems into scalable technical solutions.

Leads creative technology initiatives by building AI agent pipelines, MCP servers, plugins, generative video systems, and voice agents. Designs AI architectures, prototypes emerging technologies, develops scalable creative systems, and optimizes tools and campaigns using tracing and agent analytics. Partners with clients and cross-functional teams, presents to executive stakeholders, mentors team members, and balances brand, audience, business, creative, and technical objectives.