> Markdown version of [/jobs/ext/2720596-ml-infra-engineer](https://www.wearedevelopers.com/jobs/ext/2720596-ml-infra-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Infra Engineer - **Company:** Reducto, Inc. - **Location:** United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Systems Engineering, Computer Vision, Computer Clusters, Distributed Computing Environment, Document Type Definition, Python (Programming Language), Machine Learning, Node.Js, Open Source Technology, SimpleText, Workflow Management Systems, Graphics Processing Unit (GPU), Document Metadata, Large Language Models, Kubernetes - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/machine-learning-infra-engineer-reducto-8103163 ## About the Role * 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. * Have 3+ years of experience. 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. ## Description 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., * 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., PDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that's simply impractical for use in digital workflows. This isn't an inconvenience-it's a critical bottleneck that leads to dozens of wasted hours every week. Traditional approaches fail at reliably extracting information in complex PDFs OCR and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with. Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you: * Accurately extract text and tables even with nonstandard layouts * Automatically convert graphs to tabular data and summarize images in documents * Extract important fields from complex forms with simple, natural language instructions * Build powerful retrieval pipelines using Reducto's document metadata * Intelligently chunk information using the document's layout data ## Related Videos - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Stop using Node.js like in 2020! What changed and what you can do today with Node.js](https://www.wearedevelopers.com/videos/100011-stop-using-node-js-like-in-2020-what-changed-and-what-you-can-do-today-with-node-js) - [LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices](https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) - [Stop Using Node.js Like It’s 2020! - Alfonso Graziano](https://www.wearedevelopers.com/videos/1863-stop-using-node-js-like-it-s-2020-alfonso-graziano) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)