Technical Program Manager Senior

About The Role

As the first Senior Technical Program Manager at Fundamental, you will be responsible for the "how" and "when" of all product and engineering releases. You’ll work closely with our Senior Product Manager (responsible for the "what" and "why"), as well as the heads of Engineering, Applied AI, Research, and the FDE team in the field. You will serve as the link between three highly skilled teams that currently coordinate mainly through goodwill and Slack, and now require a structured system.

This is a hands-on position. You will not lead a team of TPMs; instead, we are seeking someone who enjoys engaging directly: standardising tools, prioritising bugs, managing the SDLC, and providing leadership with a candid assessment of progress.

What You'll Own

• Toolset standardisation. You will consolidate tools to as few systems of record as possible, establishing a single source of truth for SDLC. This is a Day-one priority.
• SDLC end-to-end. Design and own Linear, collaborate to set realistic deadlines, enforce them, and provide leadership with an accurate six-month forecast of capacity across Applied AI, Engineering, and Research. Design automated development workflows that can keep pace with AI-accelerated engineering, ensuring that compliance, security, and privacy reviews are inherently included in the system.
• The FDE-to-Engineering handoff. Formalise the process that brings FDE-built extensions into the core platform. Determine when extensions are productised, when they are deprecated, and when their functionality is integrated into the core model.
• Cross-team coordination. Run the operating rhythm that connects Applied AI, Engineering, and Research. Kill downtime caused by blocked dependencies across SF, Europe, and Israel.
• API & Technical Specification Alignment. Act as the facilitator for cross-team technical definitions. Prevent delivery bottlenecks by ensuring early agreement on API signatures, terminologies, and system design before Engineering is tasked to build.
• Delivery accountability. When a capability is built but stalled before launch, you are the one who gets it over the line.
• Operational reviews. Establish and run a weekly or biweekly review with engineering leadership to surface what’s on track, what’s blocked, and what requires an executive decision.
• Collaboration. Work across departments (Marketing, Sales, etc.) to ensure collaboration, communication, and coordination.

What We're Looking For

• 7+ years in technical program management, engineering management, or a hybrid role at a high-growth software or AI company. Most of your experience should be in environments where engineering outpaced process.
• Direct experience with the machine learning or LLM development lifecycle. You don’t need to be a researcher, but you should be knowledgeable enough to challenge engineering estimates effectively.
• Track record of standing up SDLC and tooling at a company with 50 to 300 people. Bonus if you have migrated an organisation away from tool sprawl to a unified stack.
• Comfort with ambiguity and authority. You will be coordinating senior engineers and researchers without direct management. Influence is your only tool.
• A Swiss Army Knife Operator. You are a Director or Principal-level IC who enjoys engaging with details. You design systems that speed up delivery without adding heavy corporate processes. You influence through expertise and results, not through headcount.
• Experience coordinating engineering teams across multiple time zones, ideally including the US/Europe/Israel triangle or similar.
• A bias for shipping. You measure your own impact by what is delivered, not just what is tracked.
• Strong written communication skills. Much of the cross-team alignment at Fundamental will happen in documents and asynchronous updates.

Nice to Have

• Background in enterprise B2B SaaS, especially at companies with both a platform approach and a Forward Deployed Engineering or solutions motion (Palantir, Databricks, Snowflake, Scale, Sierra, and similar).
• Experience working with an FDE or applied team where customer-built work must be productised.
• Familiarity with model deployment, inference infrastructure, or MLOps at production scale.