ML Ops Engineer, Quantum Machine Learning

4+ years building and operating machine learning pipelines or data platforms in production

Strong Python, including the scientific and ML stack (NumPy, pandas, PyTorch or JAX, scikit-learn)

Data engineering across batch and streaming workloads: pipeline orchestration (Airflow, Dagster, Prefect or equivalent), schemas, data validation and versioning

Practical MLOps: experiment tracking, model registries, reproducible training, CI/CD for models, and monitoring after deployment

Sound grasp of ML methodology, particularly evaluation: train/test hygiene, cross-validation, leakage, and how to build a benchmark that stands up to scrutiny from a customer's own data scientists

Containers, and running workloads on infrastructure shared with other teams

Git workflow, code review and testing, applied to research code as well as production code

Working with data that carries commercial or regulatory constraints

Working directly with commercial customers on their data and their problems

Clear technical writing, and the ability to explain a result and its caveats to people who are not ML specialists

MLflow, or a comparable experiment tracking and model registry stack (Weights & Biases, Neptune)

Feature stores, or any system where derived features need provenance back to their inputs

Time series or signal data, which is where much of our customer work sits

Reservoir computing, kernel methods, or other approaches that treat a fixed nonlinear map as a feature generator

Quantum computing exposure, including any of Qiskit, PennyLane, Cirq or similar (no physics degree required)

Hardware-in-the-loop pipelines, or scheduling work against instruments and devices with queues and downtime

HPC and batch schedulers such as Slurm, and GPU cluster work

Model serving at low latency (Triton, Ray Serve, TorchServe or equivalent)

Data engineering at scale (Spark, Dask, DuckDB, Parquet and columnar formats)

Cost and performance tuning across compute, storage and accelerator time

Rust or Go alongside Python

SQC is an equal opportunity employer. We value diverse perspectives and experiences, and encourage applications from candidates who may not meet every listed requirement. If you’re excited about the role and believe you can contribute, we encourage you to apply.

This position may require access to export-controlled information or technology. Employment may be subject to applicable export control laws and may require eligibility assessment based on factors such as nationality, citizenship, or residency, and, where necessary, obtaining relevant export licenses or approvals.

SQC was founded by renowned physicist and materials scientist Michelle Simmons, who pioneered the field of atomic electronics, including the development of the world’s first single-atom transistor and the first integrated circuit built with atomic precision. Our Chair, Simon Segars, former CEO of Arm, is a leader in the semiconductor industry and was instrumental in developing the processors that powered the mobile computing revolution.

As a full-stack company with in-house QPU manufacturing, SQC can design, produce and test new quantum chips in under a week, enabling rapid iteration and a decisive advantage in the race to build the world’s first commercial-scale quantum computer.

SQC is a high-accountability environment built on a simple principle: Every Atom Counts. If you’re looking to play a meaningful role in building the next frontier of computing, we’d love to hear from you.