Top AI Research Jobs in Denver & Boulder, CO

Conduct research and development for spatial intelligence products by designing, implementing, evaluating, and improving advanced algorithms. Prototype techniques to automate production processes, lead technical design studies, contribute to engineering and R&D proposals, and solve complex technical problems. The role requires an advanced degree, strong algorithm development and coding skills, and effective communication. Experience with computer vision, photogrammetry, remote sensing, Python, and AWS is preferred.

Leads enterprise AI research and development strategy, combining classical and generative AI into production-grade platforms, agentic systems, and customer products. Builds and scales teams of applied scientists and engineers, advances hybrid AI architectures, establishes experimentation and evaluation practices, and embeds responsible AI principles. Partners with senior leaders, academia, and industry to commercialize research and deliver measurable business impact.

Lead a research engineering team developing synthetic data pipelines, AI benchmarks, evaluation harnesses, and reinforcement learning environments. Partner with research scientists and frontier AI labs to design rigorous experiments, validate data quality, measure model impact, and publish findings. Responsibilities include hiring and coaching engineers, setting technical standards, building scalable Python-based systems, reviewing code, directing research agendas, and determining whether model improvements are statistically meaningful.

Research and develop AI models for music generation, transformation, remixing, and creative interaction. Explore generative audio, music understanding, and audio representation learning; design evaluation frameworks for quality, musicality, controllability, and creative usefulness; and translate research into production-ready tools. Collaborate with engineers, music technologists, producers, and musicians while applying responsible AI practices involving rights, consent, attribution, and data provenance.

Lead frontier AI safety research focused on CBRNE and cybersecurity risks. Responsibilities include managing red-team datasets, evaluation protocols, technical risk reports, QA reviews, expert teams, client engagements, threat modeling, and architecture-level mitigation recommendations across text, vision, and audio systems.

Evaluate and improve coding agents by assigning real software engineering tasks, reviewing model-generated work, completing real coding problems, and identifying model failures. The role is a fixed-pay contract offering $6,000 per week for 40 hours, with flexible scheduling and ongoing weekly work. Candidates need at least four years of professional software engineering experience, a computer science or related degree, U.S. work authorization, strong written English, and availability for 40 hours weekly.

Lead research at the intersection of AI, cybersecurity, and computer systems. Define research problems, develop hypotheses, build prototypes, design rigorous experiments, evaluate AI and security methods, and translate findings into practical systems. Research areas may include AI agents, autonomous penetration testing, attack-path reasoning, cyber deception, AI safety, and security evaluation. Mentor junior researchers, collaborate with engineering and product teams, and communicate findings through reports, presentations, prototypes, or publications.

Conduct cross-disciplinary research in scientific machine learning and cross-scale predictive modeling. Develop multimodal, surrogate, transfer-learning, physics-informed, statistical, and hybrid models that combine heterogeneous evidence across experimental scales and conditions. Build models capable of predicting at unseen physical or operational scales with sparse data, represent changing system contexts, and distinguish transferable from non-transferable factors. Collaborate across disciplines, contribute to software development, communicate research findings, and use Python and version control.

Explains how to locate people on social media using reverse image and facial-recognition search, highlights limits of Google Images and TinEye, and recommends Face2social for matching faces across platforms, even from old photos.

Designs, validates, and scales AI-powered research methods and products using statistical and behavioral modeling. Builds prototypes, develops validation frameworks, uses AI agents and LLM workflows, and converts methodological advances into client-ready offerings. Also manages team metrics, contractors, operational automation, and internal training to improve AI fluency and adoption.