Engineering Manager

Feanix is adding its first management layer. Today every engineer reports directly to the CTO. That has worked because the team is small and senior, and it stops working soon: the constraint we're hiring for is people-management capacity, not technical leadership.

We're hiring two Engineering Managers, one for each of our two teams:

• Platform Engineering — the data engineering side: ingestion from farm systems, the pipelines that process everything from farm records to genomic sequencing data, and the core data model that is our source of truth. This team owns everything through cleaned, joined, model-ready data.
• Product Engineering — everything the farmer experiences: prediction models, the decision engine, our decision-QC layer, and the farmer-facing surfaces that deliver and justify those decisions. A deliberately full-stack team spanning ML-flavored backend work and product engineering.

You can apply to either team or let us match you during the process. Each team operates as a PM/EM pair: your product counterpart owns what to build, you own how it gets built and who builds it.

Before we opened this search, our engineers wrote down what they want from an engineering leader: honest, timely feedback in both directions. Meetings that earn their time. Clarity in murky situations. Calm during incidents. Hard decisions are made, so they feel backed up rather than exposed. Notice what's on that list: they asked for the conditions to move fast, because we're bringing new decision-making tools to a traditional industry, and farmers are waiting on what we ship. This is a team that's asking to be supported well. Your mandate from the CTO is to provide that support in service of outcomes: you own delivery, and the trust described above is what makes that pace sustainable.

This role's technical engagement isn't a coding percentage, and it isn't traditional code review. Software development is being rebuilt around agentic coding, and quality now comes from what happens before and around the code: strong specs, verification harnesses, and development processes designed for engineers working through agents. Nobody has this fully figured out and that includes us. Your job is to figure it out with the team: how do we deliver what customers need, quickly, through agentic development, without sacrificing quality? You need enough engineering depth to lead that credibly, not to be the strongest committer in the room.

What You'll Do

• Manage and grow engineers. Run substantive 1:1s, give accurate and timely feedback in both directions, build individual growth plans, and manage performance. Having hard conversations early, before they become a crisis.
• Resolve conflict. Surface disagreement rather than letting it fester, distinguish healthy technical debate from interpersonal friction, and get teams back to being productive fast. You'll be the person who notices the tension in the room and does something about it.
• Own team process. Design the operating rhythm of your team planning, standups, retros, whatever the team actually needs. You treat process itself as something you test, measure, and iterate rather than install once and defend.
• Set goals and measure achievement. Contribute building a working framework for translating strategy into engineering delivery. We care that you can set goals, measure honestly against them, and report at the right level of abstraction, not which acronym you use.
• Develop AI-native quality processes. Build the practices that make quality a property of the system rather than heroics. This includes strengthening spec-driven development, testing, verification, and release discipline for a team that builds through agentic coding, where the leverage point is the spec and the harness, not the line-by-line review.
• Hold and coach the AI-first bar. Our engineers direct AI agents against real work and build verification harnesses to check that work — that's the hiring bar and the working style. You'll coach engineers up to it and hold it in review. Verification fluency isn't a side skill here: our product is a decision system that has to defend its output to farmers, and the same muscle applies.
• Use AI in your own craft. You already use AI in the work of supporting people — preparing for hard conversations, synthesizing feedback signals, drafting growth plans, analyzing team health — and you have opinions about where it helps and where it absolutely doesn't.
• Execute hiring. Own your team's hiring end to end: shaping roles with the CTO, running loops, calibrating interviewers, and closing candidates.
• Be accountable for delivery. Partner with your PM to commit to outcomes and hit them, and name the tradeoffs explicitly when you can't.

What You Bring

• 3+ years managing software engineers, having reached a senior engineering level as an IC first. You have been deep enough development to lead it credibly as AI reshapes it
• A track record of growing engineers you can point to: people you hired, coached through hard stretches, and promoted
• Experience hiring in a high-growth environment — building a team while the org scaled.
• Experience building team processes and quality/testing practices from scratch or near it — you've done this at a stage where nothing existed yet, not just inherited a working machine
• Fluency with goal-setting and measurement frameworks, and the judgment to fit the framework to the team rather than the reverse
• Demonstrated conflict-resolution skill. You have examples where you turned a struggling team dynamic around
• Daily, load-bearing AI use in both engineering and management work
• Calm under pressure, honest by default, and willing to speak up and say the hard thing.

Preferred Qualifications

• Experience as the first management layer in a startup and splitting and evolving teams as this happened.
• Experience managing teams that ship ML/AI or data-intensive products
• For Platform: background in data engineering or data-intensive systems, bioinformatics/genomics is a bonus, not a requirement. For Product: experience shipping ML- or LLM-backed product features.
• Interest in agriculture, biology, or working close to a physical industry

Logistics

• Remote, with an ideal cadence of one week per month on-site at our headquarters in Scotts Valley, CA
• Travel up to 20-25% of the time, including customer farm visits