Jobs at Hammerhead AI

Own end-to-end customer deployment programs from contract execution through production go-live. Coordinate engineering, product, vendors, infrastructure partners, and enterprise customers while managing milestones, budgets, risks, integrations, and executive communications. Build repeatable deployment playbooks and TPM frameworks, maintain technical fluency in data center power, cooling, and compute infrastructure, and support post-deployment adoption, customer satisfaction, and product improvement.

Lead commercial strategy and go-to-market execution for AI infrastructure solutions. Own the sales pipeline from prospecting through technical discovery, solution design, proposals, and closing. Analyze AI compute, GPU supply, power, and data center trends; partner with product and engineering on customer-driven requirements and messaging; and build sales collateral, case studies, competitive positioning, and objection-handling frameworks in a 0-to-1 startup environment.

Own the vision, roadmap, requirements, and execution for an AI infrastructure orchestration platform managing power, cooling, and compute. Partner with engineering, reinforcement learning, deployment, customer success, and go-to-market teams. Lead customer discovery, technical tradeoffs, product metrics, positioning, release planning, and creation of product management processes from scratch.

Build and maintain industrial IoT connectors linking Hammerhead’s orchestration platform to data center power, cooling, compute, and BMS assets. Develop hardware abstraction layers, normalize multi-vendor telemetry, implement fail-safe controls, optimize concurrent Golang data pipelines, and troubleshoot network and protocol issues during field deployments.

Design, train, and deploy reinforcement learning agents that optimize power and compute resources across physical data centers. Develop multi-agent, model-based, and deep RL systems; build simulations; transition models into stable production deployments; improve strategies for peak shaving, workload shifting, and thermal management; and collaborate with platform engineers on APIs, telemetry, and infrastructure.

Build high-fidelity digital twins and scalable physics-based simulations of data center power, cooling, and compute systems. Integrate asset models into simulation platforms, validate models against operational data, create APIs and infrastructure for reinforcement learning agent training, and optimize simulation performance. The role requires programming in Python, Go, or C++, first-principles modeling, and familiarity with machine learning environments.