Member of Technical Staff - HU

Stack

Responsibilities

• ▹Design evals for state-of-the-art coding models
• ▹Develop novel post-training paradigms grounded in formal methods
• ▹Build tooling to deliver correctness guarantees in production software engineering
• ▹Help shape the research vision and develop new capabilities at the intersection of training approaches and formal methods

Requirements

• ▹Domain expertise in either machine learning or formal methods, with active interest in learning the other
• ▹Evidence of extremely fast learning of deeply technical subjects
• ▹Experience running machine learning experiments, ideally at scale
• ▹Experience post-training large language models
• ▹Strong software engineering practice: advanced git workflows, testing, containerization, code review
• ▹Familiarity with MLOps tools and training across multi-GPU clusters
• ▹Understanding of specification-aware programming (Verus, Dafny, TLA+), proof assistants, and verification tools (LEAN, Isabelle)
• ▹AI-native, with experience using AI-assisted programming tools (Claude Code and similar)

Nice to have

• ▹Active contribution to formal verification or program synthesis projects (Verus, Lean, Dafny, or similar)
• ▹Experience running production back-end services at scale
• ▹Accountability for distributed systems and their failure modes (concurrency, consensus, partial failure)

About the company

Reasonable is an applied AI research company building formal verification for post-human software development — a compact, talent-dense technical team with deep expertise in machine learning, formal verification, and mathematical models of program semantics.