Senior Software Engineer, Platform
Platform Software Engineer at Reducto owns the infrastructure behind the company's document-understanding API: scaling Python services, queues, and model-serving paths on Kubernetes for better latency, reliability, and cost. It's a high-leverage, in-person role at an early-stage AI startup's San Francisco office.
- Compensation
- $200K–$325K / yr
- Location
- San Francisco Office
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 5+ years building, hardening, and scaling production backend/platform systems
- Hands-on experience with distributed systems, Kubernetes or similar orchestration, cloud infrastructure, observability, and incident-driven reliability work
- Strong in Python, or deeply expert in another backend language and excited to work primarily in Python
- Experience with Terraform/IaC, containerized deployments, and production debugging workflows
- Highly fluent with LLM coding tools such as Claude Code, Codex, Cursor, or similar, used daily
- Has led projects or small teams, raised engineering quality, and communicates clearly with founders, engineers, and customers
Nice to have
- Built infrastructure at an early-stage or high-growth startup
- Worked on AI infrastructure, model-serving, agent platforms, developer tools, or high-throughput APIs
- Meaningful experience with Terraform, Kubernetes, queues, Postgres, observability, or cloud cost optimization
- Comfortable talking with customers or translating customer pain into platform improvements
- Keeps up with modern AI/LLM tooling and uses it to improve engineering workflow
Full description
About Reducto
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.
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.
Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital.
The Opportunity
As a Software Engineer on Platform, you’ll own the infrastructure behind Reducto’s core API: the systems that make document understanding fast, reliable, and cost-efficient at scale. You’ll work across Python services, queues, model-serving paths, Kubernetes/container infrastructure, observability, and developer tooling to make Reducto’s platform increasingly bulletproof as usage grows.
This is a high-leverage infra role. The ideal person has built and operated real production systems, cares deeply about latency/reliability/cost, is comfortable leading technical direction for a small team, and uses modern LLM coding tools fluently to move faster.
We would love to meet you if you:
- Have 5+ years building, hardening, and scaling production backend/platform systems.
- Have hands-on experience with distributed systems, Kubernetes or similar orchestration, cloud infrastructure, observability, and incident-driven reliability work.
- Are strong in Python, or deeply expert in another backend language and excited to work primarily in Python.
- Have used Terraform/IaC, containerized deployments, and production debugging workflows.
- Are highly fluent with LLM coding tools such as Claude Code, Codex, Cursor, or similar, and use them as part of your daily engineering loop.
- Have led projects or small teams, raised engineering quality, and can communicate clearly with founders, engineers, and occasionally customers.
The core work will include:
- Scaling Reducto’s core API and document processing pipelines for higher throughput and lower latency.
- Reducing infrastructure and model-serving costs without sacrificing quality or reliability.
- Improving Kubernetes/containerized deployment workflows, observability, alerting, and operational safety.
- Building internal tooling, evals, and debugging workflows that help engineers find and fix failures quickly.
- Partnering with ML/backend engineers to optimize LLM/model-serving paths in production.