Machine Learning Infrastructure Tech Lead

The Opportunity

As our ML Infrastructure Tech Lead, you'll own the systems that make high-performance model training and inference possible at Reducto.

This is a deeply hands-on role: roughly 80% of your time will be spent building, debugging, and optimizing our infrastructure. The remaining 20% will focus on setting technical direction - identifying bottlenecks, planning our infrastructure roadmap, and helping the ML and Platform teams make strong architectural decisions.

You'll work across the stack, from model-serving kernels and GPU utilization to distributed systems and Kubernetes. We're looking for someone with the experience and judgment to lead ambiguous, high-impact infrastructure projects while remaining close to the code.

This is a fully in-person role at our San Francisco office.

What You'll Do

• Own the technical direction and roadmap for Reducto's ML infrastructure.
• Build and maintain our training and inference stack, balancing fast experimentation with high-performance production serving.
• Optimize model serving at every layer, including kernels, runtimes, batching, scheduling, and distributed inference.
• Design systems for reliable multi-node, multi-GPU training and inference.
• Improve GPU utilization, latency, throughput, reliability, observability, and cost efficiency.
• Develop benchmarks that identify bottlenecks and guide infrastructure investments.
• Evaluate state-of-the-art advances in training and inference and apply the ones that matter.
• Build the tooling and abstractions that help ML engineers move quickly from experiments to production.
• Partner with ML and Platform engineers on architecture, capacity planning, and technical prioritization.
• Raise the engineering bar through design reviews, mentorship, and hands-on technical leadership.

You'll Thrive Here If You

• Have 5+ years of experience building production infrastructure, including significant ML systems experience.
• Have led complex technical projects from an ambiguous problem through production deployment.
• Are equally comfortable setting direction and personally implementing the hardest parts.
• Have strong Python and systems-engineering skills.
• Understand the performance characteristics of modern GPU training or inference workloads.
• Are comfortable with Kubernetes and distributed training or serving frameworks.
• Can reason across low-level model performance and higher-level platform architecture.
• Hold yourself to a high bar for quality, precision, and operational reliability.
• Operate well in a fast-changing, high-growth environment.
• Take full ownership from strategy through execution.

Bonus Points If You

• Have optimized or implemented CUDA, Triton, or custom model-serving kernels.
• Have contributed meaningfully to frameworks such as vLLM, SGLang, PyTorch, TensorRT-LLM, Ray, or related open-source systems.
• Have operated distributed inference or training across hundreds or thousands of GPUs.
• Have built observability, scheduling, or capacity-management systems for GPU workloads.
• Have experience at an early-stage or high-growth startup.
• Care deeply about connecting technical excellence to measurable business impact.

Benefits

• Unlimited PTO, because great work requires recharging.
• Daily Lunch, enjoy free lunch with teammates in the office.
• Commuter Reimbursement, we’ll cover your transportation costs.
• Comprehensive Insurance, medical, dental, and vision.
• Health and Wellness Budget, up to $150 per month for wellness spending such as gym memberships or fitness classes.
• Parental Leave, flexible scheduling that works for you and your family.Working at Reducto

• This is an in-person role at our San Francisco office.
• We're an early-stage company, which means the role requires working hard and moving quickly.
• Please only apply if that excites you.