AI Engineer | Rust | Python | TypeScript | Agentic Dev Tools | Must Have Startup Experience
Location: San Francisco, CA / New York, NY (Hybrid preferred; remote for exceptional US-based candidates)
Package: $280,000 – $400,000 + competitive equity
Eligibility: Open to candidates with existing work authorisation - US Residents Only
Tech Stack: Rust | Python | TypeScript | AWS | Terraform | Sentry | Claude Code | Cursor | GitHub Copilot
🚨 Please only apply if you have commercial experience with all of the following 🚨
- Production-grade backend engineering in Python, Rust, and/or TypeScript
- Daily use of agentic coding tools (Claude Code, Cursor, Copilot, or equivalent) as your primary dev workflow
- Experience building AI development infrastructure beyond personal use — harnesses, agent skills, eval frameworks, automation scripts adopted by a team
- 5+ years shipping production systems
- Startup / scale-up experience
- Strong engineering fundamentals — architecture judgement, complex codebases, quality discipline under velocity
🚀 Join an investor-backed, high-growth software company building infrastructure that serious engineering teams depend on. This is not a role where AI is bolted on — it's embedded in how the team ships, every day. You'll own features end-to-end, build agentic development infrastructure for the whole organisation, and help define what high-velocity, high-quality engineering looks like when agents are the default.
The talent bar is exceptionally high. The problems are genuinely hard. The opportunity is significant.
Required Background
- 5+ years of professional software engineering, building and shipping production systems
- Production experience with Rust, Python, and/or TypeScript — strong in at least one, fluent across the stack
- Daily use of agentic coding tools as a primary development workflow — not surface-level Copilot use
- Proven delivery of AI dev infrastructure a team actually adopted
- Strong engineering fundamentals: architecture judgement, ability to reason across complex codebases, quality discipline when code generation is fast and cheap
- Clear communicator who can document workflows in ways engineers genuinely use
ℹ️ Very Important Notes
- Core product engineering role — not ML research, not data science, not frontend-heavy profiles
- Must be genuinely AI-native: you ship faster because of how you use agents
- Startup mindset is non-negotiable — high autonomy, fast iteration, no hand-holding
- Tech lead experience is a meaningful bonus, but engineering depth is the core requirement
Must-Haves
- Rust, Python, and/or TypeScript across production backend systems
- Agentic tooling (Claude Code, Cursor, GitHub Copilot, or equivalent) used daily — with a real point of view on how it should work inside a professional engineering team
- Experience building team-facing AI infrastructure: harnesses, context engineering, evaluation frameworks, automated feedback loops
- Ability to ship at high velocity without sacrificing quality
- Background in developer tooling, CLI engineering, or infrastructure is a strong signal
Bonus Experience
- Familiarity with reproducible build systems or package management tooling
- Tech lead experience — owning delivery and leading a team through execution
- Open-source contributions to AI tooling, agent frameworks, or developer infrastructure
- Experience at a mix of large structured companies and fast-moving startups
- Public work, writing, or content that demonstrates genuine passion for agentic development
Hands-On Experience With
- Rust, Python, TypeScript — across CLI tools, backend services, and infrastructure
- Agentic coding tools as a default engineering workflow
- AI development infrastructure: harnesses, rules files, evaluation frameworks, automated feedback loops
- AWS, Terraform, and modern cloud infrastructure
- Code review, architectural decision-making, and production system ownership at pace
What You'll Be Doing
Product Engineering
- Design, build, and ship platform features — owning them end-to-end from design through production
- Write production code using agentic tools as your default workflow, focused on the most complex and ambiguous parts of the system
- Own the quality, reliability, and architecture of what you ship
AI Infrastructure
- Build and maintain the team's agentic development infrastructure — harnesses, context engineering, automated feedback loops, and evaluation frameworks
- Evaluate new agentic tools and practices as the landscape evolves — run experiments, measure results, make evidence-based calls on what to adopt
Team Enablement
- Help engineers across the team develop sophisticated, reliable AI-assisted workflows
- Document what works, build shared practices, and codify lessons so they compound
- Exercise judgement on when agents accelerate real work — and when they introduce risk or false confidence
What They're Looking For
- A deeply technical engineer who is genuinely AI-native — not a dab