Top AI & Machine Learning Jobs

Research Scientist developing robot autonomy software and learning algorithms for dexterous manipulation in unstructured environments. Responsibilities include reinforcement and imitation learning, high-performance inference, production robot data pipelines, distributed policy training, edge deployment, and safety-critical software. The role also advances robotics research through novel methodologies while applying results to customer use cases.

Design, develop, and maintain scalable backend systems for a composite AI platform (agent-based systems, LLM integrations, RAG). Own technical vision and roadmap, make architecture decisions, establish engineering best practices, provide senior technical leadership, and deliver high-impact production software.

Build and lead the applied science function for a market simulation product. Own scientific scoping, experimental design, interpretation standards, reproducible analysis, customer studies, and evidence quality. Partner with founders and deployment leaders to translate executive questions into defensible experiments and decision-ready insights. Establish research protocols, validation standards, engagement criteria, hiring plans, and spending priorities while remaining hands-on with quantitative research and customer delivery.

Lead engineering and deploy scalable AI agents and agent orchestration at scale. Prototype GenAI solutions, design and operationalize distributed batch/real-time data flows, implement memory/RAG and vector DBs, integrate tool-calling/APIs, build calibration and guardrails, drive cloud adoption, and collaborate with global teams.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

AI Enablement Engineer who helps non-engineering Builders turn AI prototypes into secure, scalable, tested, observable, and maintainable production applications. Responsibilities include architecture guidance, technical coaching, code and security reviews, deployment, reusable templates and guardrails, production support models, and occasional end-to-end strategic builds. The role partners with engineering, security, and data teams while advancing AI application standards and Builder self-sufficiency.

Designs, builds, and scales AI-enabled tools, agentic workflows, analytical components, and data innovation frameworks for clinical, real-world, and operational data. Partners with scientists, engineers, product teams, and external collaborators to pilot emerging technologies, evaluate unconventional data sources, and support scientific decision-making. Provides innovation expertise and mentorship while ensuring traceability, human oversight, data privacy, security, and regulatory compliance.

Support design, testing, and validation of prompts and AI workflows for LLMs; help integrate speech-to-text and text-to-speech modules; contribute in agile cycles and document validation processes and best practices.

Lead the design, development, and deployment of autonomous AI agents and multi-agent systems. Build agentic workflows using planning, reasoning, memory, tool calling, RAG, vector databases, and orchestration frameworks. Integrate agents with enterprise systems and APIs while implementing evaluation, monitoring, observability, guardrails, and secure production practices. Provide technical leadership and collaborate across product, engineering, and business teams.

Conduct applied research on agentic AI systems, including memory, routing, autonomy, evaluation, reliability, and inference efficiency. Translate research into production-grade Python services, Node.js/TypeScript SDKs, platform components, and pluggable backends. Build observable prototypes, collaborate on platform integration, document findings, contribute to publications, communicate technical trade-offs, and mentor junior engineers.