Tech Lead

Parakeet Health Engineering Leadership Opportunity

Honest take: We've invested heavily in internal LLM harnesses and tools to empower our engineers to scale our product far beyond what any sane observer would think our team size should handle. What won't scale with LLMs is the the hands-on mentorship, leadership, and focus on raising the bar for a rapidly growing engineering org. We're looking for a player-coach to not only contribute to core technical decisions, but also to continue pushing the bar when it comes to craft, attention to detail, and process beyond lines of code.

You'll be an engineering leader first - emphasis on "engineering." Making key technical decisions, shipping platform + customer code, and driving ML experimentation will always be part of the role.

Your job, more broadly, is to help lead our two engineering pods (Platform and FDE) - focusing on leading by example with the IC work you do, and being a resource for both technical and career mentorship. You'll focus on investing in our people, especially as the team grows. You'll be a guiding voice in team meetings, sprint + roadmap planning, and 1:1s will be critical as the org scales.

For the next 6 months, your role will be 70% IC work and 30% technical leadership. As you settle into the role, the goal is to invert that distribution to 70% leadership and 30% IC work. You're a fit if you want to go into engineering management in the next 12-18 months - as we rapidly scale, that is the trajectory of this role.

Specific parts of our product you'll own and continue to push quality on:

Voice and conversation design at scale. Our agents speak to tens of thousands of patients daily - under the hood our platform is an audio streaming service at scale. Between infra and the core agent platform, there are endless challenges that arise in production. The gap between "technically correct" and "actually human" is enormous, and it's not a problem you can prompt your way out of.

Building the infrastructure that makes all of this possible. Getting an agent to fix a bug autonomously sounds simple. Getting it to do that reliably, on a production codebase that handles real patient data, is a different problem. It requires evaluation systems that can tell the difference between a good fix and a confident wrong answer, context that's rich enough for the agent to understand what it's touching, and guardrails that fail safely when it doesn't. We've built a lot of this. There's more to build. And the honest truth is that the agents are only as trustworthy as the humans who designed the systems around them.

You question things. This is the single most important quality we're looking for - you genuinely don't accept the status quo when you can see a better way, and you want to empower others to constantly be better. You're the counterbalance to people who defer too quickly, and you make the team better for it.

You care about the people you work with and genuinely want to see them succeed even outside of the company's trajectory.

You treat LLMs as force multipliers and you're already working that way.

You have taste. It's the thing that's hardest to hire for and most important to get right. It means you can tell the difference between code that's technically correct and a patient experience that's genuinely good. You notice the thing that's slightly wrong and then you can't unknow it (and you won't ship until it's right). You understand that what we're building is a science and an art, and you take both seriously.

You care about shipping things that reach real people, not abstract users. That context should inform how you think about every decision you make here. If it doesn't, this probably won't be the right fit.

You're honest about what you don't know. "You might have a point; I may be wrong" should be something you've said recently and meant. We debate, we disagree, we challenge each other, but we do it with respect and with the assumption of good intent. Someone who can't say they're wrong won't last long here, and frankly won't enjoy it much either.

You know there's unglamorous work and you don't think it's beneath you. No matter your seniority, everyone rotates through the mundane stuff. That's just how it works at this stage, and the people who are great here are the ones who do it without making it a thing.

You shouldn't apply if:

• You're drawn to heavy ML research. That's not what this is, and it would probably frustrate you. We're building products that leverage models, not the models themselves, and a generalist engineer with strong instincts and genuine curiosity will outperform an ML specialist here almost every time.
• You want someone to define the problem in detail before you start. We're moving too fast for that, and the most interesting problems here are the ones nobody has fully defined yet.
• You need a detailed roadmap three years out. We shoot for a north star but we're constantly adapting. Agility is our advantage.

Our honest assessment: this team skews high EQ. (Maybe too high. We probably need more shoelace staring.) But it's also what makes working here feel different from most engineering environments: people genuinely like each other, go climbing together, form bands, bake for each other.

You have 8+ years of engineering experience and at least 1 year in a leadership role: EM, TL, Head of Eng, CTO, or something equivalent. You're backend-leaning full stack, strong in Python and hands-on experience in infra and scaling. You have a CS or STEM degree, or the kind of practical experience that makes the degree irrelevant. Experience in a compliance-heavy or regulated industry is a plus, not because we need someone who knows the acronyms, but because you'll already understand why the constraints are real. And last but not least, you have a genuine belief that agentic tools are the future of how software gets built.

We move quickly: you'll hear back within 48 hours after each round. This can be as fast as two weeks end-to-end.

1. 30-minute founder intro call
2. 1-hour technical phone screen: you'll problem-solve through an evolving technical challenge
3. Full-day onsite in San Francisco. We'll work through real customer problems together. If your schedule allows, we'd love to make this a paid 3-day work trial. We think it's the best way for both sides to know if there's a real fit.
4. Reference checks → Offer

Excellent health, dental, and vision insurance

Free dinner

Free illy espresso, coffee, and beer

Fitness stipend

Commuter benefits

Unlimited PTO

401(k)

Relocation stipend

In-person, Downtown SF by Montgomery BART. After years of remote work, we wanted to be in the same room again to brainstorm, whiteboard, and actually enjoy each other's company. There's high ownership, no BS meetings, and a ton of gourmet snacks. Once a week we also get out of the office for a team lunch… because some of the best conversations happen away from the whiteboards.

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