Job Description
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/GCP
• Build 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 PennyLane
• Reduce 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 Role
• Work 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
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/GCP
• Build 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 PennyLane
• Reduce 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 Role
• Work 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
💡 Quick Summary
Seeking a career-building opportunity? The GPU Optimization Engineer (ML Infrastructure) position is now open for candidates interested in the IT Engineer & Developer Jobs sector. This role in San Francisco offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in IT Engineer & Developer Jobs is a plus.