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Mithrl — Senior/Staff Full Stack Engineer

Summary

Mithrl is a biotech AI startup seeking a Senior/Staff Full Stack Engineer to build and scale their scientific decision engine for drug discovery. The role involves end-to-end ownership of features using Python, Django, and React in an on-site San Francisco environment.

Mithrl — Senior/Staff Full Stack Engineer

Type: Full-time | On-site | San Francisco, CA Compensation: $175K–$270K + 0.2–0.6% equity Hiring count: 8 Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers). [Conflict: intake call said open to new H-1Bs too. Confirm.] Reports to: Hiring Manager (LinkedIn)

About Mithrl

Mithrl is a Series A biotech AI startup building a "Scientific Decision Engine" for R&D. Using only natural language, its platform builds custom discovery-analysis workflows (omics and beyond) with full transparency and reproducibility, on-demand in minutes rather than weeks. The platform is used daily by researchers at major pharma and industrials including J&J, Roche, Eli Lilly, AstraZeneca, and Siemens.

Founded: 2023 | Team size: 25 | Total funding: $30M Series A (~$100M valuation), led by Obvious Ventures Industry: Software Development, AI, Life Sciences, Enterprise Website: mithrl.com Office: San Francisco, CA

Why Candidates Should Join

  • Real impact: The platform is used daily by world-class researchers at J&J, Roche, Eli Lilly, AstraZeneca, and Siemens, compressing months of drug-discovery analysis into minutes.
  • Hypergrowth: 12x YoY revenue growth ($0 to $2M in year one, on track past $10M this year); three enterprise deals closed in a single week.
  • Greenfield ownership: Build V1/V2 of a product enterprise pharma is already paying for, with end-to-end ownership from concept to production.
  • Career accelerator: Engineering scaling from 10 to 30 by EOY and 60–70 the year after; tech-lead and manager paths are imminent.

Intake Call Summary

  • Series A biotech startup accelerating drug discovery with AI; $30M Series A led by Obvious Ventures at ~$100M valuation.
  • Team of 25, all SF-based, strong preference for 5 days/week in office.
  • Hiring a Full Stack Engineer with backend expertise, senior-to-staff level, to build V2 of the product (front and back end).
  • 4+ years experience, ideally startup or product-focused background. (Note: the Role Requirements badge lists 10+ years as Required. See flags.)
  • Python and React preferred; open to bioinformatics/biochem backgrounds who moved into software.
  • Salary $175K–$280K, equity 0.3–0.6%. Open to relocation and visa sponsorship, including new H-1Bs.
  • Looking to hire 8–15 engineers by EOY; engineering is the bottleneck; security and scalability are critical for enterprise.
  • Ideal profile: curious, positive, a builder (not a maintainer), comfortable with ambiguity.
  • Interview: screen with Kelly, then optional CEO chat (Vivek), then two technical Zoom rounds, then onsite.

The Role

A Senior/Staff Full Stack Engineer to build foundational systems across frontend and backend as Mithrl scales its core product to meet surging enterprise demand.

What You'll Be Doing

  • Build new product features and systems from scratch across frontend and backend
  • Design, build, and own scalable backend services, APIs, and data pipelines across the application, identity, data-storage, and ML layers
  • Ship in fast sprint cycles, contributing to production within your first few weeks
  • Collaborate with engineers, PMs, designers, and AI scientists to turn complex scientific workflows into intuitive, high-performance software
  • Own features end-to-end from development through production, iteration, and scale

Tech stack: Python, Django, React, GraphQL, AWS, GCP, Azure, SQL, REST APIs

Qualifications

Seniority

  • 10+ years professional full-stack or backend engineering, not including internships [Required] (conflict: 4+ YOE stated in the job description, intake, and the "Senior/Staff" title. See flags.)

Work Experience

  • Built products at early-stage or high-growth startups, preferably as a founder or early employee [Must have]
  • Shipping and owning production systems used by real customers [Must have]
  • Designing and working with scalable systems, databases, and data-intensive applications [Required]

Education

  • BS in Computer Science, STEM, or a related technical field [Required]

Hard Skills

  • Strong proficiency in modern frameworks like Python and React [Must have]
  • Building backend services and REST APIs; Django a plus [Required]
  • Familiarity with GraphQL and cloud infrastructure like AWS [Strongly preferred]

Soft Skills

  • Intellectually curious and excited to learn; asks questions and engages deeply with the product and problem space [Required]
  • Positive and collaborative; thrives in a high-communication, in-person team culture [Required]

Miscellaneous

  • Based in or willing to relocate to SF and work 4–5 days in-office [Must have]
  • Demonstrated interest in life sciences, drug discovery, or computational biology [Strongly preferred]

Traits to Avoid

  • Repeated job hopping: multiple stints under 8 months
  • Only life-science / big-pharma experience (e.g. Genentech, Roche); not the software background sought
  • Big-tech-only backgrounds with no product-forward or startup exposure

Role Details

  • Salary: $175K–$270K (intake mentioned up to $280K)
  • Equity: 0.2–0.6% (why-join copy and intake said 0.3–0.6%)
  • On-site policy: 5 days in-person, SF office near Montgomery BART (requirement states 4–5 days)
  • Visa sponsorship: Open to visa transfers (OPT, H-1B transfers); intake also mentioned new H-1Bs
  • Employment type: Full-time
  • Location: San Francisco, CA

Screening Questions

  1. Are you based in or willing to relocate to San Francisco for an in-person (4–5 days/week) role?
  2. What is your salary expectation?
  3. How actively are you exploring new opportunities?

Interview Process

Stage 1 — Submit candidate The hiring manager notifies whether they want to proceed.

Stage 2 — Recruiter Screen (30 min) Background, motivation, and basic fit. Assesses startup experience, interest in Mithrl's mission, and compensation expectations.

Stage 3 — CEO Intro (Optional, 30 min) Optional informational with CEO Vivek on the product, technology, and company trajectory. Most candidates opt in.

Stage 4 — Technical Interview 1 (45 min) Conducted by the CEO. Coding fundamentals via a live Python exercise on CodePad, plus a walkthrough of 2–3 past projects.

Stage 5 — Technical Interview 2 (45 min) A deeper, tailored coding challenge on CodePad based on the candidate's lean (frontend, backend, or infra). No LeetCode. Conducted by the CEO.

Stage 6 — On-Site (2–2.5 hrs) In-person at the SF office. No coding: whiteboarding, software design architecture, and a show-and-tell of 1–2 projects.

Stage 7 — Reference Check Can run in parallel with the offer stage.

Stage 8 — Offer Extended (Willem Hartog Urreta)

Stage 9 — Candidate Hired

Ideal Companies & Backgrounds

Updated Apr 25, 2026

High-growth Bay Area product-focused startups (Series A–C) with strong full-stack cultures Watershed, Lumos Labs (Lumosity), Vercel, Retool, Replit, Linear Technology, Airtable, ZipRecruiter, Notion, Coda

Mid-stage startups past early traction, where engineers built 0-to-1 products Ramp, Brex, Flexport, Vanta, Ashby, Instawork, Optomi, Webflow, Mercury, Hearsay Systems

AI/ML startups with data-intensive, product-facing platforms Cohere, Baseten, Weights & Biases, Snorkel AI, Modal Labs, Anyscale, Hebbia, Glean, Scale AI, Labelbox

Life sciences / biotech software (software-first, not legacy pharma IT) Benchling, insitro, Recursion, Cellarity, Deepcell, Tempus AI, Eikon Therapeutics, Atomic AI, Gandeeva Therapeutics

Developer tools and data platform startups with strong product-engineering DNA PostHog, Hasura, Dagster Labs, Supabase, Observable, Streamlit, Prefect, PlanetScale, dbt Labs, Hexaware Technologies

Non-ideal: large financial services / enterprise with legacy stacks Fidelity Investments, Bank of America, Wells Fargo, Charles Schwab, Mastercard, Bloomberg, Capital One, JPMorgan Chase, Goldman Sachs, Visa

Non-ideal: large life science / big pharma Genentech, Roche, Amgen

Avoid Expo

Ideal Candidate Profiles

For reference only. Do not source these specific profiles.

Prashant Khanduri (Rank #1) — LinkedIn Staff Engineer, Distributed Systems & Platform, 01 to Scale | SF Bay Area

  • Waterloo grad (co-op structure means ~2 years of built-in real experience)
  • At Affirm and Hearsay in their early days; strong early-stage signal
  • Went from engineering manager back to IC; prefers building
  • Former founder (Sn126, ~5 years); understands startup pace firsthand
  • Watch: 17 YOE (higher comp expectations); currently SentiLink (later-stage), confirm genuine early-stage interest

Simon S. (Rank #2) — LinkedIn Software Engineer | SF Bay Area

  • Palantir background (quality engineers there are infra/ops-strong, not traditional QA)
  • Grew from quality engineer to software engineer at Palantir
  • Left Palantir for startups (LeapYear, then Opto); 4+ years at Opto
  • Watch: Opto is pivoting to AI (confirm stack still relevant); confirm product engineering vs pure infrastructure

Sree Palaparthi (Rank #3) — LinkedIn Engineering | San Francisco

  • Joined Drafted at seed stage and received a return offer
  • 4+ years at Instawork post-acquisition; consistency and loyalty
  • CS Master's from Northeastern
  • The 'solid but not shiny' startup profile prioritized over FAANG
  • Watch: no visible promotion trajectory; low likelihood to leave, confirm he is ready to move

Rejected Candidate Feedback

  • Emphasize startup & founder experience: prioritize candidates with clear 01, greenfield building experience and long tenure (8+ months), filtering out those mostly from big tech or with short stints.
  • Ensure technical depth & end-to-end ownership: candidates must have proven production shipping of backend services, APIs, and data pipelines using Python/React/Django, with concrete impact stories.
  • Confirm SF-based / on-site alignment: only submit candidates willing and able to work 5 days in SF.
  • Reduce volume, focus on quality: slow down new submissions and double-check backgrounds against the early-stage startup mandate before scheduling interviews.

See also

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