Top AI & Machine Learning Jobs

Lead Datadog AI Research’s post-training and autonomous-agent research team. Set technical direction across reinforcement learning, simulated environments, synthetic data generation, evaluation, and agent training. Mentor researchers and research engineers while remaining hands-on in experimentation, model development, and technical problem-solving. Collaborate with Product and Engineering to commercialize research advances, contribute to publications and conferences, and open-source model artifacts and benchmarks.

Leads a 12-person data science and machine learning organization, setting strategy for predictive modeling, causal inference, AI evaluation, and ML infrastructure. Partners with product, engineering, and business leaders to apply data science to growth, retention, product optimization, support allocation, and risk modeling. Builds team structure, processes, technical standards, and culture while managing managers and senior technical contributors.

Build and operate the backend for Bilt’s AI-powered Concierge across chat, SMS, email, and iMessage. Responsibilities include agent tools and workflows, multi-agent orchestration, model routing, distributed systems, messaging, authentication, performance optimization, evaluation harnesses, and proactive engagement features. The role requires production backend experience, LLM familiarity, cloud service operation, strong distributed-systems fundamentals, product judgment, AI coding-agent usage, and clear technical writing.

Build, deploy, govern, and improve AI agents for HR workflows using Gemini Agent Builder, Claude, Codex, and MCP integrations. Partner with HR subject matter experts to redesign processes, transfer technical capabilities, monitor adoption, diagnose failures, and drive stakeholder adoption. Collaborate with AI and engineering teams on integrations and governance. The role requires hands-on GenAI building experience, technical fluency, evaluation expertise, change management, and sensitivity to people-data risks.

AI Engineer Interns will research, design, evaluate, and productionize AI-powered data capabilities. Responsibilities include running AI/ML experiments, developing agent self-improvement and continuous-learning systems, collaborating with product engineers, building evaluation and training infrastructure, optimizing performance, designing APIs, and contributing across the full development lifecycle.

Develops advanced analytics and machine learning models using structured and unstructured data to generate actionable business insights. Responsibilities include defining analytics requirements, selecting modeling methodologies, integrating models into business processes, measuring performance, enhancing analytics solutions, creating visualizations, and communicating findings to stakeholders. The role requires collaboration with application developers and independent judgment in solving technical and business problems.

Build and operate MLOps infrastructure for production AI agents, including evaluation frameworks, CI/CD quality gates, model access across cloud providers, experiment tracking, observability, versioned deployments, and cost optimization. Develop and fine-tune LLMs and machine learning models, monitor production quality and drift, and collaborate with AI engineers and clinical experts to deliver reliable healthcare claims automation.

Design, build, deploy, and operate production-grade AI agents, automations, and internal tools. Lead workflow discovery, multi-agent orchestration, context and state management, integrations, full-stack interfaces, evaluation, guardrails, testing, monitoring, and staged releases. Measure business outcomes and determine when agents, simpler automation, or no solution is appropriate. The role requires ownership of agent systems from architecture through production improvement and reuse.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Build and own production agent systems, including orchestration loops, tool use, retrieval-augmented generation, evaluation gates, and MCP servers. Design reliable control flow with retries, timeouts, state management, grounding, and failure recovery. Develop regression corpora and judge-based gates to determine whether agent-generated code and content can ship safely. Maintain APIs under malformed or hostile inputs and evolving system rules while delivering both architecture and deployed code.