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As Technical Program Manager, Handshake AI Research, you will own the execution backbone of HART’s research initiatives. You will translate research goals and partner requirements into well-structured technical programs, ensuring delivery across Research, Engineering, and Operations. This role is ideal for someone who thrives at the intersection of research ambition and operational reality — someone who can bring structure to ambiguity, manage complex cross-functional dependencies, and scale technically rigorous work into repeatable systems. You will act as the Directly Responsible Individual for research programs, ensuring alignment, execution quality, and on-time delivery for initiatives spanning data quality, evaluation frameworks, and post-training workflows.
Job Responsibility:
Own end-to-end program execution for Handshake AI Research initiatives, from early scoping through pilot, scale, and steady-state operations
Translate research objectives and partner needs into clear technical requirements, milestones, and delivery plans
Serve as the primary execution lead bridging Research, Engineering, Operations, and external AI lab partners
Drive programs across HART’s core pillars: Data Quality & Validation
Evaluation & Benchmarking
Post-training data workflows (RLHF / RLAIF / domain-expert labeling)
Manage timelines, risks, dependencies, and resourcing across multiple concurrent research programs
Partner with engineering to ensure tooling and infrastructure support scalable research execution
Collaborate with operations, quality, and analytics teams to design repeatable, high-quality research pipelines
Track success metrics aligned with research OKRs, partner commitments, and evaluation outcomes
Identify bottlenecks, propose system-level improvements, and drive continuous process optimization
Communicate program status, tradeoffs, and outcomes clearly to internal leadership and external stakeholders
Requirements:
Experience as a Technical Program Manager, Program Manager, or equivalent, owning complex cross-functional initiatives
Strong technical foundation in machine learning systems, particularly LLMs, post-training workflows, and evaluation frameworks
Experience working closely with researchers and engineers, translating research goals into executable programs
Familiarity with data annotation, data quality, or evaluation pipelines in ML systems
Proven ability to manage ambiguity, align stakeholders, and deliver in fast-moving environments
Excellent written and verbal communication skills, with the ability to explain complex technical work to diverse audiences
Systems thinker with a bias toward execution, scalability, and operational rigor
Comfort partnering with external organizations (AI labs, academic institutions, or industry partners)