Machine Learning Infrastructure Tech Lead

Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows. 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.

Responsibilities

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

Requirements

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

Nice to have

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; Daily Lunch; Commuter Reimbursement; Comprehensive Insurance (medical, dental, vision); Health and Wellness Budget up to $150 per month; Parental Leave; Equity