Vals AI — Member of Technical Staff, Platform

Vals AI — Member of Technical Staff, Platform Type: Full-time | On-site | San Francisco, CA Compensation: $150K–$210K + competitive equity (~20,000–40,000 shares at ~$10/share) Hiring count: 1–2 Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers); sponsorship available for exceptional candidates Reports to: Langston Nashold, CTO Salary flag: The posted band is $150K–$210K. The intake-call transcript notes the range was "adjusted to $210k–$215k to allow negotiation flexibility," which reads as a top-end lift rather than a full re-band. Posted band carries here; confirm the true ceiling with the client before quoting anything above $210K to a candidate. About Vals AI Vals AI benchmarks the world's leading AI models on rigorous, domain-specific tasks across finance, law, software, healthcare, and more. They run all of their own evaluations and build many of their benchmarks in-house, working with all major foundation model labs and top financial institutions. Their work has been featured in the Wall Street Journal, Washington Post, and Bloomberg. Founded: 2024 | Team size: 25 | Total funding: $5M (pre-seed, backed by Pear VC) Industry: AI, Software Development Website: vals.ai Office: SOMA, San Francisco Why Candidates Should Join Real benchmarking authority: Vals AI evaluates the top AI models on hard, domain-specific tasks and works directly with every major foundation model lab and top financial institutions. Coverage in the WSJ, Washington Post, and Bloomberg. Get in early: Pre-seed, backed by the Pear VC team in SF. Early enough to shape the engineering culture and own large chunks of the platform from day one. Build the eval infrastructure: Own the systems that run LLM evaluations at scale, spanning distributed systems, cloud infra, and full-stack features, with real autonomy over architecture and technical decisions. Pure IC, hands-on daily: No management track expected. Fuzzy, open-ended problems that you design, build, and ship yourself. Intake Call Summary Expanding the engineering team due to growth; open to candidates from both big tech and startups, filtering for clear signs of excellence on the resume. Hiring an individual-contributor role with 2–6 years of experience. Wants someone hands-on who is not looking for a managerial track. Avoid staff / principal level to fit budget and team needs. Prefers candidates with notable achievements or from top-tier schools and companies. Must have Python; React is a nice-to-have. Looking for a full-stack generalist rather than a pure-infra specialist. Comp: salary discussed with room to flex at the top (see salary flag above); equity roughly 20,000–40,000 shares at ~$10/share. Interview process unchanged from prior roles; emphasis on impressive or interesting resumes. Driven by a need to increase engineering capacity as the team expands. Ideal candidate is hands-on and capable of running code and design reviews. Not a new grad; should bring real experience and a track record of excellence. Hiring Manager Update — Aug 19, 2026 (Langston Nashold) Vals AI is prioritizing an evaluations engineer and a platform engineer right now. For Platform specifically, the traits they are filtering for: Strong coding / technical ability Experience at strong startups, or clear evidence of hands-on work at a bigger tech company Impressive, relevant infrastructure projects with real ownership (The evaluations-engineer criteria in the same update — new grad, strong internships/projects, impressive schooling, flexibility on hours — apply to that separate role, not this one.) The Role A generalist software engineer (2–6 years) joining the platform team to build the infrastructure that powers LLM evaluations at scale. You work across the stack, from Python backend services to React frontend features, shipping quickly in a high-autonomy environment. What You'll Be Doing Building and maintaining the platform that runs LLM benchmarks end to end: Python libraries, web platform, cloud infrastructure, and tooling Working across the full stack as a generalist, on backend Python/Django services and frontend React/TypeScript features depending on team needs Taking fuzzy, open-ended problems and independently designing, building, and shipping solutions Performing code reviews and architecture reviews for other engineers on the team Collaborating closely with the research team to ensure infrastructure meets their evaluation needs Tech stack: Python, Django, React, TypeScript, AWS Qualifications Seniority 2–6 years of experience in full-stack or platform engineering [Required] Work Experience 0-to-1 product-building experience in big tech (e.g. FAANG, Bloomberg, NVIDIA), a VC-backed startup, or as a founder [Required] Founded a company or worked at an early-stage startup [Strongly preferred] Experience building systems of meaningful scope in production [Strongly preferred] Education Undergrad CS degree from a top-25 program; a top-5 program is required if experience is not strong [Strongly preferred] Hard Skills Production Python expertise in a professional setting [Strongly preferred] React/TypeScript frontend experience [Strongly preferred] Traits to Avoid Engineers who no longer code daily or mainly delegate Candidates seeking management or team-lead responsibilities Profiles with only non-technical companies (e.g. Wells Fargo, PayPal) and no top school Role Details Salary: $150K–$210K (see salary flag re: top-end) Equity: Competitive; roughly 20,000–40,000 shares at ~$10/share On-site policy: 5 days in-office in San Francisco, CA Visa sponsorship: Open to visa transfers (OPT, H-1B transfers); sponsorship available for exceptional candidates Employment type: Full-time Location: San Francisco, CA Screening Questions Can the candidate be on-site? If not, are they willing to relocate? What is their salary expectation? How actively is the candidate exploring new opportunities? Interview Process Stage 1 — Submit candidate After submission, you'll be notified if the hiring manager wants to proceed. Stage 2 — Initial Screening Call Stage 3 — Technical Round Stage 4 — Take Home + On-site Stage 5 — Offer Extended Stage 6 — Candidate Hired Ideal Companies & Backgrounds Updated Jun 15, 2026 Top-tier big tech companies (FTE only) Google, Meta, Microsoft, Stripe, NVIDIA, Bloomberg, Databricks, Cloudflare, Apple (Note: page showed "Show all 10 companies" but was not expanded before copying — 1 company missing.) YC and VC-backed early-stage startups in AI/infra Supabase, Vellum, Railwaymen, Render, Meter, Scale AI, Retool, Patronus AI, Arize AI (Note: "Show all 10 companies" not expanded — 1 company missing.) High-growth startups known for strong engineering culture Ramp, Rippling, Plaid, Coinbase, Duolingo, Discord, Datadog, Figma, Linear (Note: "Show all 10 companies" not expanded — 1 company missing.) Non-ideal companies — do not source from these: Large financial institutions and banks not known for technical excellence JPMorgan Chase, Bank of America, Capital One, Citigroup, Charles Schwab, Wells Fargo Legacy tech/fintech companies with slower engineering cultures PayPal, Oracle, IBM, Cisco, SAP, Salesforce, Workday, ADP, Intuit Large consulting and services firms (non-product engineering) Wipro, Capgemini, HCLTech, Accenture Federal Services, Deloitte, Infosys, TCS, Cognizant Ideal Candidate Profiles For reference only — do not source these specific profiles. Braden Wicker — LinkedIn San Francisco Bay Area Why a great fit: FAANG background is enough to earn an interview Area for improvement: A stronger school would be nice Note: Braden was a previous hire for this same role Hung Tran — LinkedIn eval agi @ Vals AI | prev AI infra @ Tesla | web agent @ Yutori | CS + Eng Physics | San Francisco Why a great fit: VC-backed experience and a strong internship Area for improvement: Longer tenure at a previous company would be nice Rejected Candidate Feedback Emphasize candidates with clear 0-to-1 startup experience and measurable scale ; demonstrable success building production systems from scratch is non-negotiable. Prioritize candidates with direct LLM evaluation and AI agent infrastructure experience over general data or generic platform engineering. Ensure candidates show strong, hands-on Python and full-stack implementation skills with clear, concise technical communication. Look for authentic signals of excellence from top-tier companies or research that map clearly to the high-autonomy, production-scale platform requirements.