Top AI & Machine Learning Jobs

Run and evaluate machine learning training experiments for autonomous robots, curate and clean real-world fleet data, improve data-labeling structures, track model performance metrics, and collaborate with Teleoperations to improve data collection. The role focuses on production ML for perception, decision-making, and control, including imitation learning, reinforcement learning, and deployment evaluation on live robotic systems.

Leads architecture and development of a production agentic AI platform. Defines technical strategy, engineering standards, reliability and model evaluation practices, and scalable AI capabilities. Designs RAG integrations, third-party data connections, model routing, tool use, and code execution systems. Partners across Engineering, Product, and Data to drive adoption, resolve technical challenges, and align AI investments with customer outcomes while contributing directly to product delivery.

The Senior Data Scientist will analyze large-scale customer data, investigate behavioral questions, design and automate studies, and translate findings into actionable recommendations. The role includes maintaining analytics frameworks, documenting methodologies, writing white papers, and communicating insights across the company. Candidates should be independent, comfortable with ambiguity, and experienced with SQL and Redshift. Python or R, machine learning, Spark, A/B testing, and cross-functional experience are preferred.

Year-round intern supporting AI, automation, and analytics initiatives for business and manufacturing teams. Responsibilities include analyzing processes, preparing and validating data, developing workflows with ChatGPT, Alteryx, SQL, Snowflake, Python, Power Automate, and Tableau, creating reusable AI prompts and custom GPT solutions, documenting processes, testing solutions, and communicating outcomes to stakeholders.

Build, evaluate, integrate, and operationalize machine learning, deep learning, and generative AI capabilities for federal mission applications. Responsibilities include developing models and datasets, creating AI services and interfaces, integrating APIs and pipelines, deploying scalable and reproducible solutions, documenting workflows, and improving capabilities through technical and user feedback. The role collaborates with software and data engineers, technical leaders, subject-matter experts, and mission users in a controlled government environment.

Build and operate multimodal LLM platform capabilities, including knowledge ingestion, embeddings, retrieval-augmented generation, evaluation, feedback loops, deployment, monitoring, and latency optimization. The role spans cloud, on-premises, and air-gapped environments, integrating AI knowledge systems with 3D scene representations and real-time training applications. Responsibilities include productionizing AI updates, improving reliability, and collaborating with spatial computing, mobile, and product engineering teams.

Conduct frontier AI research on model capabilities, safety, evaluations, security, and governance. Independently design and maintain research code and experiment pipelines, implement and adapt ML papers, develop experiments from ambiguous questions, publish findings, and help shape AIUC’s public research agenda and strategic direction.

Identifies opportunities to apply AI and automation, designs and tests AI-powered workflows, documents and improves business processes, builds dashboards, analyzes operational data, measures productivity gains, and supports implementation, communication, and change management across business units.

Own end-to-end machine learning systems for email threat detection, from data strategy and modeling through production deployment, observability, capacity planning, and rollout. Set architecture and technical direction for model serving, retrieval, GPU workloads, and LLM-based systems. Benchmark alternatives, resolve production incidents, establish reproducible ML standards, mentor engineers, and communicate technical recommendations to product and executive leadership.

Leads the enterprise AI platform and transformation roadmap, moving generative AI from experimentation into secure, scalable production. Oversees AI architecture, LLM applications, RAG, agents, data integration, governance, reliability, monitoring, adoption, and value realization. Partners with executive, engineering, security, privacy, legal, compliance, quality, and finance teams while leading internal teams and strategic vendors. The role requires extensive experience building healthcare AI products and large-scale machine learning infrastructure in regulated environments.