Experience Senior · 6+ yrs (5+ years)
About the role
structured by ORIAbout ReductoReducto 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…
What you will 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.
What they are looking for
- 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.
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, and vision)
- Health and Wellness Budget up to $150 per month
Pythonsystems-engineeringdistributed systemsCUDATritonmachine learningKubernetesvLLMSGLangPyTorchTensorRT-LLMRayGPU
Full posting text
$200K–$325K; offers equity.