Top Tech Jobs & Startup Jobs in Denver & Boulder, CO

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Design and deliver distributed storage systems for AI and machine learning workloads at hyperscale. Lead architecture, development, testing, deployment, and innovations in scalability, performance, security, and efficiency. Collaborate with AI researchers and cloud infrastructure engineers, mentor engineers, and guide technical strategy for high-availability, durable, high-performance storage supporting training and inference.

Leads complex, cross-organizational technical programs connecting customer needs, AI innovation, cloud solutions, and business outcomes. Defines program vision, roadmaps, metrics, governance, and business cases; drives architecture, execution, risk management, and executive alignment. Partners with customers, engineering, product, security, and field teams, leads strategic engagements, improves operational processes, champions responsible AI and security, and mentors program managers.

Build and optimize inference systems and tools for generative AI models, collaborate with researchers to improve performance, profiling, debugging, and deploy large-scale distributed inference solutions.

Lead the vision, strategy, roadmap, and global execution of AI-powered learning and workforce skilling products. Manage and mentor product managers, collaborate with engineering, design, research, marketing, and customer teams, and drive products from customer insight through launch and scale. Build AI-native experiences, improve product adoption and metrics, foster inclusive team practices, and deliver measurable outcomes for millions of learners.

Design and build petabyte-scale multimodal data pipelines and infrastructure (text, audio, images, video). Own and maintain systems like Spark, Ray, and vector databases, partner with pre/post-training teams to improve data recipes through experimentation, and support large-scale dataset storage and processing for AI model training.

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.

Design, implement, and optimize distributed pre-training infrastructure for large-scale GPU clusters. Profile, benchmark, and debug compute, memory, networking, and storage bottlenecks; optimize communication libraries and GPU programming stacks; collaborate with hardware and model teams to scale training, ensure reliability, and influence architecture and roadmap.

Lead hardware-software co-design for datacenter-scale AI systems, analyzing real workloads to drive architecture, performance, and cost improvements; prototype and productionize optimizations across accelerators, runtimes, compilers, interconnects, and distributed training/inference; mentor engineers and influence hardware and platform roadmaps.

Design, build, benchmark, and operate large-scale reinforcement learning and pretraining systems on NVIDIA and AMD GPU clusters. Optimize distributed, parallel software for training models, gather compute-roadmap data, and collaborate to deliver production-grade conversational and generative AI features.