Machine Learning Infrastructure Tech Lead

1 мес. назад

200k–300k USD / yearUSALeadOnsite

kubernetesgpudistributed systemsinfrastructuremachine learning

Lead and build high-performance infrastructure for model training and inference at .

Обязанности

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

Будет плюсом

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.

Условия

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
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.
Equal Opportunity is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration without regard to sex, race, color, age, national origin, religion, disability, sexual orientation, gender identity, veteran status, or any other protected category.

Другое

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.
We’ve grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms.
has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital.
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.
Impact: Your work directly shapes how the world’s best AI companies access and use enterprise data.
Speed: We move fast, ship often, and iterate in days, not months.
Learning: Work alongside world-class engineers, operators, and founders who care deeply about product, precision, and velocity.