Technical Project Manager

Role: Technical Project Manager

Department:
Applied AI

Location:

SF Bay Area, onsite

Level: Mid level

We're looking for a Technical Project Manager to support technical project execution excellence across our Applied AI and Core Engineering teams. You'll own project decomposition, estimation, and timelines. You'll coordinate cross-functional teams, surface and remove blockers, and ensure smooth execution from development through deployment.

This is intense hands‑on work that is foundational and core to business. You'll support competing demands between product and engineering, and execute against shared priorities. You're comfortable operating in ambiguity and shipping iteratively with accelerated schedules.

• Delivery Support: Manage project timelines, scope, estimation, decomposition and dependencies across project development initiatives. Work with engineering and client delivery teams to understand timelines and deliverables. Anticipate, identify and elevate blockers to keep projects on track.
• Coordination & Communication: Align core engineering, Dev Ops, testing, and client delivery teams. Surface risks early, manage cross-team dependencies, and communicate status clearly to stakeholders.
• Program Tracking & Visibility: Maintain project tracking and status reporting. Ensure teams have clear sprint goals, dependencies are visible, and progress is tracked against timelines.
• Technical Triage: Understand enough about agent orchestration, infrastructure automation, workflow orchestration, CI/CD pipelines, and platform architecture to ask good questions and spot risks. Deep expertise is not required; strong pattern recognition is.

• 2-4 years of experience in program/project management, ideally in technical environments (software, infrastructure, or consulting).
• Proven track record to manage project timelines and track progress across distributed teams
• Clear written and verbal communication. You're concise and precise in your writing and feedback.
• Thrives in ambiguity. You ask good questions and elevate appropriately.

• AI development, Agent Orchestration & Workflows:
  Familiarity with orchestration concepts (multi‑step agentic workflows, state management, error handling). You can follow discussions about agents, tools, and execution paths.
• Infrastructure Automation:
  Basic understanding of infrastructure‑as‑code principles. You understand the difference between imperative and declarative approaches. You can reason about reproducibility, versioning, and scalability.
• Workflow Orchestration:
  Familiarity with the problem space of durable, distributed workflow execution. You understand how orchestration layers fit into broader architecture.
• CI/CD & Dev Ops:
  Understand deployment pipelines, infrastructure automation, and release management. You can reason about testing strategies and deployment workflows.
• Product & UX/UI:
  You understand how user experience impacts adoption and team velocity. You can spot friction in product flows and collaborate with design teams.

• Strong organizational and framework driven prioritization skills. You manage competing demands and communicate clearly.
• Data‑driven mindset. You track and reference metrics to inform decisions.
• Attention to detail. You document work clearly and catch gaps in communication or scope.
• Comfort with rapid iteration and learning. You move fast and own mistakes.

• Experience managing AI/ML projects or infrastructure teams.
• Background in startup or scale‑up environments (fast‑paced, high‑ownership culture).
• Hands‑on coding experience with AI agents, LLM applications, or Python.
• Basic familiarity with…