ML Infrastructure Engineer - Quantum AI

ML Infrastructure Engineer (Junior) Kadence Talent is partnered with a quantum hardware company building at the frontier of quantum-accelerated AI to find their next ML Infrastructure Engineer.The RoleThis team is doing genuinely novel work, quantum circuit simulation, large-scale numerical optimization, tensor network contractions, and AI model training and they need someone to make the compute infrastructure that powers it all just work. That means reliable GPU access, reproducible experiments, and workloads that scale without researchers having to become cloud experts.You'll own the full stack from cloud provider configuration to the Python tooling researchers use to launch jobs. It's a high-ownership role at a small, fast-moving team where your work will be immediately visible and impactful.What You'll DoBuild job submission tooling and compute abstractions that handle diverse workloads, GPU simulation, distributed training, high-throughput CPU jobs, across PyTorch, JAX, and scientific computing frameworksSet up experiment tracking and reproducibility infrastructure so research is auditable and repeatableManage and optimize cloud spend across multiple providers, track credits, burn rates, and flag problems before they surfaceBuild CI/CD pipelines for research workloads: automated testing, benchmarks, and artifact managementSupport cross-functional teams beyond the core research group, including finance ops and hardwareWhat We're Looking ForA solid foundation in cloud engineering : AWS or GCP, compute, storage, IAM basicsSome exposure to ML or scientific computing environments (doesn't have to be deep)One area you've gone deeper: containerization, GPU instances, job schedulers, MLops toolingCurious, responsive, and comfortable asking questions when you're in new territoryFresh graduates and early-career engineers welcomeNice to HaveHands-on experience with PyTorch or JAXFamiliarity with tools like MLflow, W&B, or similar experiment tracking platformsGitHub Actions or container-based CI/CDAny exposure to HPC job schedulers or hybrid cloud/on-prem setupsComp range in the target of $180k-$240k