Staff Software Engineer, ML/AI Calibration

D-Wave•New Haven, CT

11h•$175,000 - $241,000

About The Position

D-Wave is seeking a Staff Software Engineer for its Calibration, Automation, and Tools team to design and build intelligent software that improves calibration and operation of quantum processing units (QPUs). The role combines strong software engineering with hands-on machine learning and artificial intelligence experience to turn experimental data into reliable models, automated workflows, and tools used by scientists and engineers. Working closely with physicists and quantum, software, firmware, and hardware engineers, the Staff Software Engineer will take solutions from exploration and simulation through validation and production deployment. As an individual contributor, this person will shape a single technology or product line and remain hands-on in delivering ML and AI improvements to calibration quality, reliability, and efficiency. The role includes leading projects in QPU calibration and automation software; developing and evaluating machine-learning approaches; improving data and model pipelines; integrating validated models into operational workflows; and building services, APIs, data stores, and interfaces. The engineer will also help improve team quality practices and mentor colleagues.

Requirements

MS or higher in Computer Science, Electrical or Computer Engineering, Mathematics, Physics, or a related field, or equivalent relevant professional experience.
At least 6 years of professional software development experience, including delivery of production systems and contributions to technical decisions within a team.
Demonstrated ownership of machine-learning initiatives from problem definition and model development through rigorous evaluation, operational deployment, and measurable outcomes.
Strong Python skills and experience with scientific computing and ML libraries such as NumPy, pandas, SciPy, scikit-learn, PyTorch, or TensorFlow; proficiency in another language such as Go, Java, C, or C++.
Practical understanding of statistical modeling, optimization, experimental design, and model evaluation, including data leakage, overfitting, uncertainty, and reproducibility.
Experience translating scientific or engineering problems into ML tasks, choosing suitable baselines, and explaining results and limitations to domain experts.
Experience building maintainable services and REST APIs, using SQL and relational databases such as PostgreSQL, and developing interfaces with React, Angular, Vue.js, or comparable technologies.
Experience with Git, code review, CI/CD, AWS, and automated unit, integration, and end-to-end testing.
Familiarity with LLM-based coding assistants and sound judgment in reviewing their output for correctness, security, and maintainability.

Responsibilities

Lead one or more projects within QPU calibration and automation software, coordinating cross-functional work with scientists and engineering teams as needed.
Develop and evaluate machine-learning approaches for calibration and operations, including parameter estimation, surrogate modeling, anomaly detection, drift detection, and adaptive experiment selection.
Contribute to process improvements for reproducible data and model pipelines, including data validation, training, versioning, deployment, and monitoring.
Partner with scientists to define meaningful metrics and compare models against established numerical and statistical methods using simulation, historical data, and controlled hardware experiments.
Integrate validated models into calibration workflows with appropriate operational limits, fallbacks, and traceability; investigate model failures and changing data distributions.
Architect and maintain Python services, REST APIs, relational data stores, and user interfaces that make experimental results and model behavior understandable and actionable.
Contribute to evolving technical processes and quality practices, helping improve testing, observability, and reliability within the team.
Mentor junior and new team members, participate in design reviews, and independently verify AI-assisted code and analyses for soundness.