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

Lead product definition and execution for AI-powered assistant systems. Define end-to-end requirements, translate model capabilities into product decisions, design evaluations, balance trade-offs (quality, latency, cost, reliability), and work closely with ML, backend, and mobile engineers to ship reliable, user-trusted AI features.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Build and maintain cross-platform Electron desktop apps focused on reliability, performance, and real-time behavior. Implement desktop features (file access, notifications, auto-updates), integrate AI via backend APIs, optimize startup/memory, profile Electron overhead, manage large local state and message history, and ensure stability on macOS and Windows.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Work directly with leadership to drive high-priority cross-functional initiatives across engineering, product, hiring, operations, and business development. Lead projects end-to-end, build teams and processes, research markets and opportunities, and implement operational improvements to deliver measurable business outcomes.

Design, build, and operate ML infrastructure for training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, and cost. Create pipelines, observability, benchmarking, and tooling to enable fast experimentation and productionization of models while diagnosing regressions and bottlenecks.

Design, build, and ship LLM-powered applications and agent workflows. Integrate models with tools, databases, and services; develop prompting, tool-calling, retrieval, and evaluation systems; improve model behavior, reliability, latency, and cost; and establish observability, experimentation, and production practices for continuous improvement.

Design, build, and deploy AI-powered internal tools and intelligent workflows to automate People, Finance, Recruiting, and operations. Identify improvement opportunities, translate business needs into technical solutions, own end-to-end execution, and collaborate across teams to ship reliable production software and AI integrations.

Lead discovery and delivery of AI-powered internal tools that automate workflows across People, Finance, Recruiting and Operations. Translate business needs into technical solutions, design intelligent automations and integrations, and own end-to-end execution from concept to production in a small, hands-on team.