Top AI Research Jobs in Denver & Boulder, CO

Conduct hands-on research with frontier AI labs to evaluate model behavior, identify failure modes, and design interventions that improve reliability. Responsibilities include designing evaluations, building data pipelines and fine-tuning workflows, collaborating with internal and external stakeholders, and addressing challenges involving long-context reasoning, tool use, and real-time feedback loops.

Research Scientist responsible for RL-based post-training of large-scale video generation models. The role includes preference optimization, online RL, video reward model development, human and automated evaluation, reward-hacking safeguards, and secondary distillation of aligned models. Candidates need experience with generative modeling or post-training, reinforcement learning, diffusion or flow-matching models, PyTorch, and distributed training. Preferred experience includes visual-generation reward models, VLM-based judging, preference data collection, and video-specific temporal and physics failure modes.

Conduct open-ended AI research focused on end-to-end training workflows using LanceDB. Design replicable experiments and benchmarks, demonstrate workflows across AI domains, develop content, publish research and models, and collaborate with engineering and product teams. The role requires deep learning expertise, experience training state-of-the-art models, open-source software development, strong prioritization, product awareness, and staying current with AI research.

Conduct original research on reliable agentic AI, developing methods for simulation, evaluation, optimization, regression control, and continuous learning. Analyze agent failures, traces, and human feedback; create experiments and prototypes; and translate research into production-facing systems. Collaborate with product and engineering teams, contribute to technical strategy, and provide rigorous experimental evidence. The role requires strong research depth, publication or open-source contributions, hands-on Python development, and expertise in LLM agents or agentic systems.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.

Develop spatial AI and perception systems for autonomous mobile robots operating in dynamic warehouses and yards. Train and evaluate models using real multimodal fleet data, improve 3D scene representations and state estimation, and develop evaluation methods. Take research concepts from literature through on-vehicle validation and productionization, collaborating with software and test engineering on data, annotation, calibration, monitoring, and simulation infrastructure.

Annotates data accurately according to project guidelines, performs quality checks, provides feedback on annotation tools and processes, collaborates with project teams, maintains efficiency, and reports issues for resolution.

Leads Mercury’s cross-line AI/ML research capability across insurance products. Responsibilities include setting strategy, building a research tooling team, modernizing workflows, developing model monitoring and AI infrastructure, establishing responsible AI guardrails, partnering across technology and data science teams, driving adoption, and coaching talent. The role requires strong quantitative, insurance analytics, Python, machine learning, generative AI, and LLM evaluation expertise, along with substantial people and cross-functional leadership experience.

Conduct research and develop novel AI algorithms for unsupervised learning and perception at scale. Extend proprietary algorithmic tools, maintain deep learning experimentation systems, and collaborate with software and autonomous vehicle engineers to deploy algorithms on internal and customer vehicle platforms. The role emphasizes rapid experimentation, computer vision, neural networks, and practical delivery of customer projects.

Lead the development and release of AI safety and security benchmarks every two to three weeks. Own evaluation taxonomies, harnesses, quality standards, verification, publication decisions, research timelines, and quarterly roadmap planning. Collaborate with AI labs, universities, internal researchers, and freelancers. The role also requires staying current with AI safety research, maintaining lab relationships, presenting work to clients, and attending conferences.