Antes — Founding Engineer, Applied AI

Type: Full-time | On-site (SF in-person highly preferred; open to exceptional remote) | San Francisco, CA Compensation: $180,000–$250,000 + 0.1%–1% equity Hiring count: 2 Visa sponsorship: Yes — H-1B, O-1, OPT Reports to: TBD

About Antes

Antes is a stealth-stage startup building AI for hardware engineering at complex manufacturers — automating engineering compliance and accelerating innovation at the intersection of AI, hardware, software, law, and policy. They partner with some of the world's most iconic automotive, aerospace, and industrial manufacturers and are backed by General Catalyst, Kleiner Perkins, and the parent company of Ferrari. The founding team spans AI, advanced engineering, and policy, including a former America's Cup world champion, a former global legal compliance lead at Uber, AI researchers from Stanford AI Lab, a former president of Hyundai, a former CTO of Caterpillar, and a former Chairman of the FAA and FCC.

Founded: 2025 | Team size: 1–10 (Seed) | Total funding: Not disclosed Industry: AI Tools Website: antes.ai Office: San Francisco, CA

Why Candidates Should Join

• Founding-level ownership: Among the first technical hires, owning mission-critical AI systems end-to-end.
• Heavyweight backing and team: Backed by General Catalyst and Kleiner Perkins, with a founding team drawn from top industry and government leadership.
• Real-world impact: Build AI that integrates directly into the engineering environments of iconic automotive, aerospace, and industrial manufacturers.

Intake Call Summary

• No intake call transcript provided — the role page includes an Intake Video only. Request a summary or transcript from Contrario if intake detail is needed.

The Role

Antes is hiring a Founding Applied AI Engineer to build the core systems behind its automation engine for hardware engineering — designing, implementing, and post-training AI agents that power critical product-development workflows for global enterprise customers. This is a deeply technical, high-impact role; you'll be among the first technical hires and will own mission-critical systems end-to-end.

What You'll Be Doing

• Design and implement core workflows and infrastructure for hardware engineering agents, including intelligent context management, novel indexing approaches, and post-training
• Build agents and ML models that interpret, reason over, and manipulate complex engineering design data, integrating with enterprise-grade engineering tools and systems
• Work closely with customers and the engineering team to deliver AI-powered workflows that are reliable, intuitive, and trusted in production environments
• Prototype rapidly, validate ideas with real users, and ship frequently
• Influence engineering culture, architecture decisions, and the future of the product

Tech stack: Python, PyTorch, Hugging Face, W&B

Requirements

• 2-6 years of experience, open to exceptional new grads
• Strong Python and ML library experience (PyTorch, Hugging Face, W&B)
• Experience building production ML or AI systems
• Strong familiarity with LLM architectures and agent research trends
• Bachelor's or higher in CS, ML, or equivalent
• Genuine interest in technology, not for the money

Green Flags

• Experience in law/policy or customer-facing product work
• Track record of high-impact contributions or published papers at a top-tier AI lab or team, strongest signal of technical caliber
• Background in a domain with real-world constraints such as automotive, aerospace, robotics, or energy
• Experience with geometric data, CAD, or complex engineering design data, maps directly to what Antes is building
• Post-training or fine-tuning experience, critical for the agent customization this role demands
• Genuine interest in the intersection of AI, hardware development, and policy

Red Flags

• Only research or academic AI experience with no production system background, mission-critical reliability is a hard requirement
• Weak customer collaboration skills, direct customer work is core to this role
• Slow moving or process-dependent, this role requires someone who prototypes, ships, and iterates fast
• No experience with search, information retrieval, or complex data systems
• Only cares about salary of the role
• Random resume padding and random startup experience

Role Details

Salary$180,000–$250,000Equity0.1%–1%On-site policySF in-person highly preferred; open to an exceptional remote candidateVisa sponsorshipH-1B, O-1, OPTEmployment typeFull-timeLocationSan Francisco, CA

Screening Questions

• None provided on the role page.

Interview Process

Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — First Round Chat — General software engineering test and vibes check. Stage 3 — Second Round Engineering Test (70 min: 20 min Qs + 50 min coding, no AI assist) — Single practical coding question testing problem solving and understanding of APIs, databases, and other coding fundamentals. Stage 4 — Onsite (paid, all expenses covered; 1–2 days) — Working with the team in person; AI-assisted interview building something related to the Antes product. Stage 5 — Offer Extended Stage 6 — Candidate Hired — Candidate accepts and starts.

Ideal Companies & Backgrounds

Updated June 25, 2026 Target companies — Databricks, Applied Intuition, Harvey, Glean Technologies

Ideal Candidate Profiles

For reference only — do not source these specific profiles. The role page lists these under "Ideal Candidates — DO NOT CONTACT."

• Arun Patro
• Ruta Joshi
• Serena W
• Andres Montoya
• Shihao C
• Neil Dhruva
• Nursultan Soodonbekov
• Cameron Eich
• Hamza Najam
• Raghav Raj Sah
• Pranav Narala
• Jiahao Woo
• Aidan Chen
• Michael Ngo
• Idea Vanicharoenchai
• Shlok Arjun Marathe — From Australia and does TA/RA at university, which is not a popular thing there vs. the US where it reads as resume padding; an indicator he is truly into technology.
• Wiley Matthews — Community college then RPI with a 4.0 GPA.

(LinkedIn links were not exposed in the page markup — only LinkedIn buttons — so no profile URLs are available to carry over.)

Rejected Candidate Feedback

• None provided on the role page.