Forward Deployed Engineer
No salary listed
Mid
London, UK+1 moreMore locations: San Francisco, CA, USA
HybridFrequent in-person attendance is expected, with customer and team travel.
Software EngineeringAI Application Engineering
H1B Sponsorship Available
About the job
Requirements
- Time spent inside or close to a high-accountability engineering culture, understanding the value of correctness and verification.
- Familiarity with systems languages such as Rust, JVM languages such as Java or Kotlin, or cloud-native languages such as Go.
- Domain expertise in formal methods or verification, through research or practical application.
- Understanding of specification-aware programming tools such as Verus, Dafny, or Viper, and proof assistants and verification tools such as TLA+, Lean, Roq, or Isabelle.
- Experience building relationships with senior engineers and CTOs.
- Genuine interest in the commercial side of the work.
- Comfort with ambiguity, hands-on scrappiness, and rapid iteration while maintaining consistently high-quality output.
Responsibilities
- Lead discovery by joining early customer meetings, owning technical scoping, and bringing research depth to prospective partner conversations.
- Diagnose which prospective partners’ systems are suitable for verification and proof-of-concept work, ensuring proofs of concept solve real and replicable problems.
- Design proofs of concept and initial customer solutions, translating observations and requirements into tightly scoped deployments a small team can deliver rapidly.
- Identify capability gaps that recur across partners and feed those signals into research and development decisions about training and product development.
- Elicit real specifications from complex systems and engineering teams, building trust while translating between prospective partners and the frontier of AI and formal verification.
- Remain hands-on between partner conversations as a technical team member, contributing to building throughout the role.
Desired Qualifications
- Actively contribute to formal verification or program synthesis projects such as Verus, Lean, or Dafny.
- Have run production back-end services at scale and experienced the limitations of what testing can prove.
- Have been accountable for distributed systems and understand failure modes involving concurrency, consensus, and partial failure.
About the company
Reasonable AI builds AI systems for formal verification of mission-critical software. It combines language models with formal methods to generate, repair, and verify machine-checkable proofs alongside production code, using Verus. It aims to help engineers overcome the verification bottleneck by delivering proofs that scale with AI-assisted code, while analyzing failure modes to prevent cheating. Its approach emphasizes small, fast models that can approach larger systems' proof quality, enabling verifiable correctness for each line of code.
Company Size
1-10
Company Stage
N/A
Total Funding
N/A
Headquarters
London, United Kingdom
Founded
2025
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Simplify's Take
What believers are saying
- Nvidia amplified Reasonable's August 2026 proof-synthesis results across its developer channels.
- London hiring remains active, with interns joining in July 2026 and followers climbing.
- Formal verification demand rises as verified code needs six or seven proof lines each.
What critics are saying
- No customers, revenue, or external funding are disclosed on reasonable.io or LinkedIn.
- Two founders and interns signal a tiny team facing Nvidia, OpenAI, and DeepSeek.
- If proof synthesis commoditizes, Reasonable loses its research moat before 2027.
What makes Reasonable AI unique
- Reasonable fine-tuned Nemotron 3.5 Lightning on Verus proofs in August 2026.
- It nearly matched a model fifty times larger on pass@3, while running faster.
- Its research targets the formal-verification bottleneck created by AI-generated mission-critical code.
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Benefits
- Company Equity