Top AI & Machine Learning Jobs

Administer and improve enterprise AI platforms including Microsoft Copilot, Azure AI Foundry, ChatGPT Enterprise, and Claude Enterprise. Configure security, access, licensing, integrations, governance, monitoring, and cost controls. Investigate complex issues, perform root-cause analysis, develop technical standards and runbooks, manage platform initiatives, evaluate emerging technologies, and support responsible AI adoption. Collaborate with security, architecture, engineering, governance, and business teams while mentoring junior staff and providing platform guidance.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Develop scalable C#/.NET enterprise applications powered by LLMs and RAG architectures. Responsibilities include integrating AI services, building REST APIs and backend services, working with vector databases and API gateways, implementing secure authentication, optimizing performance, and supporting CI/CD deployments. The role collaborates with cross-functional teams, participates in Agile ceremonies and code reviews, writes technical documentation, and supports production systems.

Build and operate Sysdig’s AI platform, including MCP servers, agent infrastructure, AWS Bedrock runtime and gateway, usage tracking, governance, observability, security controls, and employee AI automations. The role requires production ownership, AWS platform engineering, full-stack development, LLM application experience, and strong autonomy in designing secure, scalable AI systems.

Anthrogen is seeking exceptional talent for an open-ended, full-time onsite role in San Francisco. Candidates may propose research directions, technical capabilities, or unusual combinations of skills relevant to frontier AI and engineered biological systems. Applicants should demonstrate exceptional work, independent judgment, curiosity beyond a specialty, and a concrete record of building, discovering, or delivering meaningful results.

Build and iterate AI systems spanning computer vision, image manipulation, video generation, and agentic data analysis. Own the full pipeline from model research and API integration through scalable web application deployment. Develop rapid MVPs using AI-assisted tools, orchestrate multiple models into autonomous workflows, and test solutions in e-commerce operations before scaling them into SaaS products.

Leads soccer analytics and data science workstreams supporting MLS competition, player relations, medical, and sporting decisions. Uses soccer, roster, tracking, financial, and performance data with statistical, AI, and machine learning methods. Evaluates roster rules, competition changes, player pathways, and sports science trends; develops KPIs, models, reporting, and recommendations for senior stakeholders. Also guides sporting data governance, technology evaluations, vendor use, and cross-functional analytics priorities.

Build full-stack AI applications for nuclear and energy-sector document analysis and verification. Responsibilities include architecting applications, implementing Retrieval-Augmented Generation systems, developing front-end and backend services, and deploying cloud infrastructure on AWS using ECS, S3, Bedrock, API Gateway, and Route 53. The role requires production software engineering experience, Docker expertise, and collaboration in a fast-paced innovation team.

Build and maintain backend services and APIs powering LangSmith’s AI observability, tracing, monitoring, and evaluation workflows. Responsibilities include architectural design, high-volume data storage and query optimization, reliability engineering, testing, monitoring, alerting, production troubleshooting, root-cause analysis, documentation, and cross-functional feature delivery.

Design, implement, and deploy production-scale machine learning models to evaluate disputes, chargebacks, and fraud likelihood. Build and manage orchestration layers for multiple models, collaborate with stakeholders to integrate partner/customer data, and improve automated and supervised decision-making for a product-focused ML environment.