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Open nowPosted 218 days ago

Senior Software Engineer

Turing Labs2 open roles

Pay
$170,000 – $250,000 a yearly
Where
Palo Alto, CA
Work mode
On site
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Your applicationOpen nowSenior Software EngineerTuring Labs · Palo Alto, CA
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  6. 43.5%60 days
This job: posted 218 days ago

The posting

Consumer packaged goods like sauce, cookie, and lotion are used by every single human on the planet. However, developing those is insanely hard, manual, and complex. Turing solves this by purpose-built AI (not GPT wrapper) to discover most optimal formulation, replacing decades of trial-and-error. We’re pure-play vertical AI SaaS challenging legacy incumbents in trillion dollar industry. Your code will run inside the R&D pipelines of some of the largest CPG companies on the planet — billion-dollar brands whose products are on shelves in every country.

FACT : 50,000 food and beverage products you consume are harmful. We’re here to fix that by enabling CPGs to develop better, healthier and better options using our AI platform. Your code will directly impact what 8 billion people eat and use every single day. Next release of GPT won’t kill us!.

Why This Is Hard (and Interesting)

You are building an product experience that translates ambiguous, highly complex, subjective matter expertise, and physical chemistry needs into solution for users who never had better tools than spreadsheets.

This requires going far beyond traditional statistical methods and basic LLM calls. You will own the end-to-end development of AI-powered features across the backend, frontend, and deployment layers. You will build on our proprietary ML algorithms, LLMs, internal embeddings, and structured data models into product experiences that users actually trust.

This is a zero-to-one build. You will ideate, plan, and deliver software that consistently exceeds customer expectations. You will collaborate tightly with our ML team to ensure outputs are not just mathematically accurate, but practically trustworthy in a lab.

Who We Are

We are backed by $19.75M in funding from Y Combinator, Insight Partners, and elite angels like Eric Ries.

We operate with a high-agency culture:

  • Customer Value: We talk less and ship something very fast that customers can see and use.
  • Direct Customer Interaction: Engineers talk directly to customers. There are no translation layers or PMs shielding you from the user.
  • Fast Decision Making: We debate on Monday, decide immediately, and execute crazy.
  • Results Over Process: We care about customer impact, not performative engineering practices.

We are a small, elite team. If you join, you will be part of the team, sitting next to the founder and tier 1 ML and software engineers in the Bay Area.

We have a high-agency culture:

  • Weekly Customer Value: Every week we ship something that customers can see and use
  • Direct Customer Interaction: Engineers talk directly to customers, no translation layers
  • Fast Decision Making: We debate Monday, decide immediately, execute Tuesday-Friday
  • Results Over Process: We care about customer impact, not perfect engineering practices

We should talk if you’re:

  • You are a 100x AI-Native Engineer. You wield AI coding tools (Cursor, Windsurf, Claude Code) as a force multiplier to ship at an impossible speed. But crucially, you possess the deep technical judgment to know exactly when to take the wheel back from the AI.
  • You’re a previous founder or founding engineer. You have built something from scratch that people use or pay for.
  • You’re unusually obsessed with hard problems. Your current skills or even what you've done before is less important than your ability to go all-in and accomplish anything that you set your mind to.
  • You’re comfortable with ambiguity and adapting to dynamic priorities. You need to have good judgement to know when to let little fires burn and when to immediately jump in.
  • You have excellent communication and candor. You’ll be communicating directly with customers, users, engineers and collaborating with us on key decisions.
  • You are not a pay-me-to perform type person. You’re self-motivated to solve bigger problems in the world. If that's not an exciting mission for you, it will be hard to stay motivated through the inevitable highs and lows.
  • You have grit and perseverance. You should be accustomed to picking between hard options and pushing through it.

What You Bring

  • Deep experience integrating LLMs and ML into products (e.g., LangChain, vector DBs, prompt design, model outputs into product).
  • A track record of shipping real products—ideally in vertical SaaS, internal tools, ML/analytics platforms, or enterprise software.
  • The ability to not just write code, but think in product—understanding UX, user value, and workflows.
  • A strong desire to work in-person, as an Individual Contributor, with a small team to move fast and win together.

What You’ll Do

The specifics matter less than the pattern. We are looking for someone who has shipped real things in hard domains, has genuine depth with LLM and ML integration (not just API calls), and has used AI coding tools not as a crutch but as a genuine multiplier. If you have built a product end-to-end — backend, frontend, deployment — and you have strong opinions about how to do it well, we want to talk.

Experience with vertical SaaS, internal tools, ML/analytics platforms, or enterprise software is a strong signal. So is having been a founder, a founding engineer, or someone who took extreme ownership in an early-stage environment.

Must-Haves

  • Have 3+ years of full-stack or backend-heavy engineering experience
  • Have LLM and/or ML integration experience (e.g., LangChain, vector DBs, prompt design, model outputs into product)
  • Have shipped real products — ideally in vertical SaaS, internal tools, ML/analytics platforms, or enterprise software
  • Can code, but also think in product — UX, value, users
  • Have founder experience or took extreme ownership in a startup environment
  • Want to work in-person and appreciate hanging out together; we have reasonable flexibility with WFH on a case-by-case basis.
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