Technical Program Manager

The Electric Plant•San Francisco, CA

2d•Onsite

About The Position

The Electric Plant Co. is building a new category of plant and tree intelligence. Our IoT hardware measures the hidden electrical signals in plants and trees, paired with environmental data, and our foundation model decodes those signals into real-time insights about plant health, growth, and stress. We’re a small, fast-moving company in San Francisco working at the intersection of biology, hardware, AI, and IoT. We closed our seed round in November 2025 and our first commercial deployment launches this year. The Role We're hiring a Technical Program Manager to run our research roadmap and the DGP and partner operations that make it real: the path every new capability takes from an early scientific signal, through a properly specified data generation project, through AI model development, to a validated capability our commercial team can sell. You'll be directly accountable for our data generation programs and the external research partners (CROs, university researchers) who run them, and coordinate closely with our science and AI/ML functions, whose work depends on what you deliver. This is a role for someone who can hold real enthusiasm for a promising idea while still insisting on the evidence and specification that makes it a trustworthy bet, and who stays warm with an external partner while holding the line on what we actually need. You’ll turn a scientist’s hypothesis, an ML engineer’s data needs, and a field partner’s logistics into one plan the whole team trusts. Beyond the roadmap itself, you’ll help build our research team’s credibility and reach: co-authored papers with university partners, a science advisory board, grant funding, and new research partnerships. You’ll report to our CEO and be the person who makes sure our research roadmap is a living plan the whole company can see and trust, not something reconstructed from memory in a meeting.

Requirements

Track record specifying experimental or data-generation work: defining exit criteria and success metrics, and knowing what “enough evidence” looks like.
Experience holding firm on a requirement or data-quality bar with a stakeholder or external partner who’d rather do it a more convenient way, without damaging the relationship.
Either grounding in plant physiology or agronomy, or a strong track record running rigorous scientific research operations without that specific domain background.
Real fluency in at least one of sensor hardware, experimental science, or applied statistics and data, with credible range across the others.
Experience sourcing, vetting, and contracting external research partners (CROs, university PIs, or similar) on a budget and timeline.
Experience building or materially improving roadmap, tracking, or review process, calibrated to a small team’s real needs.
Strong written communication: you can produce a document a scientist, an engineer, and a CEO all trust.
Comfort operating with no mature process to inherit.

Nice To Haves

A PhD or equivalent deep research training in a life science or related field.
Datagen-for-ML experience.
Experience at a hardware-plus-AI or sensing company.
Grant-writing experience.
Experience running a research operations function or PMO that later scaled up.

Responsibilities

Capability validation. Prove new and existing model capabilities, like drought-stress detection, hold up across new locations, tree ages, varieties, and species, ahead of commercial deployments.
Research roadmap. Track every active research bet from discovery signal to deployed capability, and know exactly what stage each is at and what's blocking the next one.
DGP execution and partner management. Source, vet, contract, and hold accountable the CROs and university researchers who run our data generation projects, and hold each DGP to spec from day 0 through wrap, catching data-quality problems mid-flight rather than after the fact.
Cross-functional coordination. Translate between our science and AI/ML functions and our external research partners into one plan everyone can execute against.
Research capacity building. Develop paper partnerships, a science advisory board, grant funding, and new research partnerships that expand what our research team can do.