[Hiring] Senior Technical Program Manager @Fundamental

Role Description

As Fundamental's first Senior Technical Program Manager, you will own the "how" and "when" of everything the product and engineering organization ships. You'll partner directly with our Senior Product Manager (the "what" and "why"), the heads of Engineering, Applied AI, and Research, and the FDE team in the field. You will be the connective tissue between three highly capable teams that today coordinate through goodwill and Slack, and need a system.

This is a hands-on role. You will not be managing a team of TPMs; we want someone that enjoys being in the weeds: standardising tools, ordering bugs, running the SDLC, and giving leadership an honest read on what is and isn't getting done.

What You'll Own

• Toolset standardization: Consolidate tools to as few systems of record as possible, with a single source of truth for SDLC. Day-one priority.
• SDLC end-to-end: Own Linear, partner to set real deadlines, hold the line on them, and give leadership an accurate six-month forward view 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 baked into the system.
• The FDE-to-Engineering handoff: Formalise the path that pulls FDE-built extensions into the core platform. Determine when extensions get productised, when they get deprecated, and when functionality folds into the core model.
• Cross-team coordination: Run the operating rhythm that connects Applied AI, Engineering, and Research. Kill the downtime on 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 asked to build.
• Delivery accountability: When a capability is built but stuck before launch, you are the person who gets it across the line.
• Operational reviews: Establish and run a weekly or biweekly review with engineering leadership that surfaces what's on track, what's blocked, and what needs an executive decision.
• Collaboration: Work across departments (Marketing, Sales, etc.) for collaboration, communication, and coordination.

Qualifications

• 7+ years in technical program management, engineering management, or a hybrid role at a high-growth software or AI company. Most of your time has been in environments where engineering moved faster than process.
• Direct experience with the machine learning or LLM development lifecycle. You don't have to be a researcher, but you should be fluent enough to push back on engineering estimates intelligently.
• Track record of standing up SDLC and tooling at a company in the 50 to 300 person range. Bonus if you've migrated an organisation off tool sprawl onto a coherent stack.
• Comfort with ambiguity and authority. You will be coordinating senior engineers and researchers without managing them. Influence is the only lever.
• A Swiss Army Knife Operator. You are a Director or Principal-level IC who loves being in the weeds. You build systems that accelerate delivery without introducing heavy corporate bureaucracy. You influence through competence and execution, not through headcount.
• Experience coordinating engineering across multiple time zones, ideally including the US/Europe/Israel triangle or equivalent.
• A bias for shipping. You measure your own work in what got delivered, not in what got tracked.
• Strong written communication. A lot of cross-team alignment at Fundamental will happen in documents and async updates.

Nice to Have

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