Machine Learning Infra Engineer

About Reducto Reducto helps AI teams ingest real world enterprise data with state of the art accuracy. The vast majority of enterprise data — from financial statements to health records — is locked in unstructured file formats like PDFs and spreadsheets. We train vision models to read those documents the way a human would, and make it possible to build products, train models, and automate processes at scale. We’ve grown incredibly quickly, growing revenue by 7x YOY, and now work with hundreds of companies ranging from leading AI teams (Harvey, Vanta, Scale), through to enterprise (FAANG, top 3 trading firm). We're raised over 100M from world class investors like A16z, Benchmark, and First Round Capital, and are hiring a Machine Learning Engineer to help us train and deploy the models critical to the performance of our core product. The Opportunity As an ML Infra Engineer , you’ll play a key role in building the inference and training frameworks that make it possible to deliver results at scale. You’ll collaborate closely with our ML and Platform teams to scale training across nodes, develop faster and more efficient serving, and create observability across the stack. This is a high-impact role where you’ll help define what high performance ML training and inference look like at Reducto. What You’ll Do Build, and maintain our training and inference stack with an emphasis for fast iteration on training + flexibility for exploring new methods and high performance in inference. Develop benchmarks for both sets of stacks to identify bottlenecks. Explore SOTA advances in training and inference and work to apply them. Design systems for scaling model training across multi-node, multi-GPU environments with strong reliability and observability. Scale distributed training and inference workloads across large GPU clusters while improving utilization, reliability, and cost efficiency. Build the tooling, abstractions, and observability that help ML engineers move faster from experiment to production. You’ll Thrive Here If You: Hold yourself to a high bar for quality and precision. Enjoy solving complex problems and building from first principles. Have strong Python skills + a background in systems engineering. Are comfortable with Kubernetes and distributed training frameworks. Love getting your hands dirty with real-world implementation challenges. Operate well in fast-changing, high-growth environments. Collaborate effectively across technical and non-technical teams. Take full ownership from strategy through execution. Bonus points if you: Have experience at an early-stage or high-growth startup. Have developed in open source training/inference stacks in a meaningful way. Are excited to set up distributed inference across 100s-1000s of GPUs. Care deeply about combining technical excellence with business impact. This is an in person role at our office in SF. We’re an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you. Benefits at Reducto Unlimited PTO: We believe great work requires recharging. Lunch: Receive a free lunch to eat with your teammates daily at the office. Reimbursed Transportation: Provide us with your receipts and we’ll take care of the costs. Insurance: Generous health insurance covering medical, dental, and vision. Health and Wellness Budget: We provide up to $150/mo reimbursement for health and wellness spending, such as gym memberships, fitness classes, or similar. Parental Leave: Work with us to build a leave schedule that works for you and your family. Reducto is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law. #J-18808-Ljbffr