Senior Scientific Software Engineer — Autoresearch

Description

The company builds systems that automate theoretical and computational research in quantum computing.

You will build a reusable autoresearch platform that proposes hypotheses, implements and tests candidates, and uses the accumulated results to decide where to explore next.

First uses include automated performance gains across the company's software repositories, discovering quantum error correction codes with QEC theorists, and searching for architectures with compiler researchers.

This is a hands-on role with significant technical ownership, combining classical search and optimization with AI agents to build methods that improve the research process over time.

Responsibilities

• Design and build the autoresearch platform, including configurable search strategies, agent and tool integration, and dependable execution of long-running experiments.
• Build and assess search and optimization methods that generate, select, refine and combine candidates within limited compute budgets.
• Work with theorists and compiler researchers to turn their questions into search spaces, constraints, objectives and runnable evaluation procedures.
• Produce reproducible evidence of progress using correctness checks, strong baselines, noise-aware measurement and independent validation, and deliver useful discoveries and software improvements.

Requirements

• Proven track record of independently delivering substantial research software or developer tools, with strong software engineering skills in Rust or a similar systems language and scientific Python.
• Deep practical experience with search and optimization, especially combinatorial optimization, evolutionary algorithms, meta-heuristics or automated algorithm design.
• Hands-on experience building automated discovery or optimization systems that generate candidates, run experiments and learn from the feedback.
• Sound experimental judgment: picking meaningful objectives, allocating evaluation budgets, diagnosing failures, and telling real improvements apart from noise or evaluation artifacts.
• Ability to collaborate with researchers from other fields and turn mathematical ideas into dependable computational tools.
• Preferred: LLM-guided search, automated algorithm design, program synthesis or autonomous research systems.
• Preferred: evolutionary computation, quality-diversity methods, tree search, hyper-heuristics or adaptive selection of search operators.
• Preferred: Bayesian optimization, bandit methods, experimental design or other ways of allocating expensive evaluations.
• Preferred: performance engineering and automated software tuning, including profiling, benchmarking and statistical handling of measurement noise.
• Preferred: infrastructure for parallel experiments, isolated execution, checkpointing and reproducible tracking of candidates and results.
• Preferred: compiler optimization, scheduling, routing or hardware-software architecture search.
• Preferred: computational algebra, constraint solving, theorem proving or formal verification.
• Preferred: quantum computing, quantum error correction, fault-tolerant architectures or quantum simulation.
• Preferred: systems that improve their own search strategies, tools or experimental workflows and reuse findings across tasks.
• Depth in a few relevant areas is valued over experience in every topic.
• Experienced engineers and researchers are welcome, including recent PhD graduates who have owned substantial implementation work. A PhD is welcome but not required.

Conditions

The approximate base salary range is $160,400 - $220,400.

Base pay is set using factors such as geographic location, role-specific knowledge, skills and experience, and the ranges are reviewed against external market data over time.

The company is an equal opportunity employer and does not discriminate on any basis protected by law.

Benefits

• All new hires receive equity grants.