Top AI Research Jobs in Denver & Boulder, CO

Develop AI models and algorithms for biomolecular modeling and simulation. Responsibilities include integrating large-scale molecular and laboratory data, writing research code, orchestrating data generation and training campaigns, developing deep learning architectures and generative models, publishing research, and collaborating with an interdisciplinary international team.

Designs scientific benchmarks and evaluations for advanced AI models, focusing on virology, microbiology, biological misuse risks, and biosecurity. Responsibilities include red-teaming models, assessing AI-generated scientific outputs, developing evaluation rubrics, analyzing results, and collaborating with AI researchers and engineers. The role requires deep biological research expertise, technical writing, and the ability to evaluate AI capabilities and risks. This is a remote U.S. contract role with occasional travel and in-person onboarding.

Leads research and development of AI, machine learning, robotics, and autonomy technologies for defense and commercial applications. Directs technical teams, advances algorithms for perception, planning, control, and mission systems, and guides technologies from research through prototype development, integration, evaluation, and delivery. Builds customer relationships, shapes technical strategies and R&D programs, manages research portfolios, and communicates complex AI concepts to executives, scientists, customers, and program leaders.

Develop and improve AI agents and machine learning models that identify financial opportunities in healthcare contracts, supply chain, pharmacy, and managed care data. Analyze complex datasets, automate manual workflows, build information extraction and agentic capabilities, and establish evaluation frameworks. Collaborate with engineering, product, value engineers, and healthcare subject-matter experts to translate research into scalable solutions with measurable financial impact.

Evaluate Bengali audio for nativeness, fluency, pronunciation, intonation, and linguistic authenticity. Document clear English-language feedback and detailed justifications, collaborate with coordinators, and complete audio review tasks accurately and on time to improve AI language understanding and generation.

Own research and implementation of post-training methods for legal language models, including supervised fine-tuning, reinforcement learning, reward design, verifiers, preference modeling, evaluations, and distributed training. Lead experiments from research direction through production deployment, while debugging training instability, reward hacking, and divergence. Build rigorous evaluation systems and production-quality Python code for long-horizon legal reasoning and agentic workflows.

Evaluate AI coding agent interactions for usefulness, correctness, reasoning quality, engineering judgment, and user trust. Provide detailed written and video feedback, distinguish response quality levels, and help define standards for tools such as OpenAI Codex, Claude Code, and Cursor. This flexible, remote contract requires strong software engineering experience, AI-assisted development expertise, and excellent communication skills.

Own product backlog and roadmap for knowledge and semantic platform capabilities (ontology, knowledge graph, taxonomy, metadata). Conduct discovery with internal clients, define semantic models, entity relationships, and requirements; partner with engineering, data, and AI teams to deliver reusable platform services; promote governance, data stewardship, validation, adoption, and metrics to improve semantic quality and AI-enabled product delivery.

Lead research and thought leadership on data management and AI readiness, produce actionable insights, advise clients and sales, present at events, mentor analysts, and improve research processes to drive client outcomes.

Conduct empirical AI safety research with mentor guidance, owning projects from hypotheses and controlled experiments through analysis and technical writing. Build and debug evaluation harnesses, agent environments, datasets, monitoring methods, and post-training experiments. Investigate model failures, alignment, deception, misuse, multi-agent risks, and evaluation reliability. Produce reproducible code, experiment logs, research analyses, and papers for potential submission to leading machine learning conferences.