GPU Optimization Engineer (ML Infrastructure)
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Kadence Talent is partnered with an AI x Quantum start-up, looking for a GPU Optimization/ML Infrastructure Engineer to make it easy for researchers to run compute-heavy workloads at scale. You'll sit between ML and infrastructure, helping scientists move from local experiments to scalable GPU-backed systems across cloud environments.
What You'll Do:
- Enable GPU access and scaling across AWS/GCPBuild simple systems to run and manage compute-heavy workloads
- Improve speed, reliability, and cost-efficiency of experiments
- Help transition workflows from Modal ? native cloud
- Support research using tools like Qiskit and PennyLaneReduce friction & make infra invisible and easy to use
What We're Looking For:
- Experience with AWS or GCP (compute, basic networking)
- Familiarity with GPU workloads (PyTorch, CUDA, etc.)Strong Python skills
- Experience with Docker (Kubernetes is a plus)
- Comfortable working in a hands-on, startup environment
Nice to Have:
- ML/AI infrastructure or training pipelines
- Distributed compute (Ray, Dask, Spark, etc.)Experience supporting researchers or data scientists
- Why This RoleWork at the intersection of quantum + ML + infrastructure
- Build systems from scratch - high ownership, high impact
- Turn unused compute into real research output
Location: This is an on-site role in Mountain View, CA
Compensation: $250k - $280k+, plus equity