Q

QuEra Computing

Cloud-access 256-qubit neutral-atom quantum computer

Senior Scientific Software Engineer - Autoresearch

AI ResearchConfirmed live in the last 24 hours
Full-timeBoston, MA, USAIn-person

About the Job

Requirements

  • 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.

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.

Desired 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.
  • Bayesian optimization, bandit methods, experimental design, or other approaches to allocating expensive evaluation budgets.
  • Performance engineering and automated software tuning, including profiling, benchmarking, and statistical treatment of measurement noise.
  • Infrastructure for parallel experiments, isolated execution, checkpointing, and reproducible tracking of candidates and results.
  • Compiler optimization, scheduling, routing, or hardware–software architecture search.
  • Computational algebra, constraint solving, theorem proving, or formal verification.
  • Quantum computing, quantum error correction, fault-tolerant architectures, or quantum simulation.
  • Systems that improve their own search strategies, tools, or experimental workflows and reuse findings across tasks.

About the Company

QuEra Computing

Cloud-access 256-qubit neutral-atom quantum computer

QuEra Computing builds quantum hardware using neutral-atom technology and offers cloud access to a 256-qubit quantum computer called Aquila, accessible through Amazon Braket. Their platform targets optimization problems common in finance, logistics, and drug discovery, such as finding the maximal independent set in a graph. The Aquila system is controlled and programmed with Bloqade, an open-source software package available in Python and Julia that lets users simulate quantum systems and operate the hardware. Unlike some competitors who focus on different qubit technologies or sell standalone devices, QuEra combines hardware access with software tools, lowering barriers for both academic researchers and commercial customers. The company aims to help users solve complex optimization tasks by providing scalable quantum hardware and supporting software, so users can experiment, test, and deploy quantum strategies on the cloud.

Company size
201-500
Company stage
Series B
Total funding
$278.8M
Headquarters
Boston, MA
Founded
2018

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