Senior Scientific Software Engineer — Autoresearch

Summary

QuEra Computing is hiring a Senior Scientific Software Engineer to build systems that automate theoretical and computational research in quantum computing.

You will develop a reusable autoresearch platform that generates hypotheses, implements and evaluates candidates, and uses accumulated evidence to guide further exploration. Initial applications include automated performance improvement across our software repositories, quantum-error-correction code discovery with QEC theorists, and architecture search with compiler researchers.

This is a hands-on engineering role with substantial technical ownership. You will combine classical search and optimization with AI agents, and develop methods that progressively improve the research process itself.

Responsibilities

• Design and build the autoresearch platform, including programmable search strategies, agent and tool integration, and reliable execution of long-running experiments.

• Develop and evaluate search and optimization methods for generating, selecting, refining, and combining candidates under limited computational budgets.

• Collaborate with theorists and compiler researchers to translate research questions into search spaces, constraints, objectives, and executable evaluation procedures.

• Establish reproducible evidence of progress through correctness checks, strong baselines, noise-aware measurement, and independent validation; deliver useful discoveries and software improvements.

Qualifications

• Strong software engineering skills and a record of independently delivering substantial research or developer tools, with proficiency in Rust or a comparable systems language and scientific programming in Python.

• Deep practical experience in search and optimization, particularly combinatorial optimization, evolutionary algorithms, meta-heuristics, or automated algorithm design.

• Hands-on experience building automated discovery or optimization systems that iteratively generate candidates, execute experiments, and learn from feedback.

• Strong experimental judgment: choosing meaningful objectives, allocating evaluation budgets, diagnosing failures, and distinguishing genuine improvements from noise or evaluation artifacts.

• Ability to work with researchers across disciplines and turn mathematical ideas into reliable computational tools.

Preferred Qualifications

• LLM-guided search, automated algorithm design, program synthesis, or autonomous research systems.

• Evolutionary computation, quality-diversity methods, tree search, hyper-heuristics, or adaptive selection of search operators.