Software Engineer, Platform

Reducto • San Francisco, CA

2d • $200,000 - $325,000 • Onsite

About The Position

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.

Requirements

• 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.
• Used Terraform/IaC, containerized deployments, and production debugging workflows.
• Highly fluent with LLM coding tools such as Claude Code, Codex, Cursor, or similar, and use them as part of your daily engineering loop.
• Led projects or small teams, raised engineering quality, and can communicate clearly with founders, engineers, and occasionally customers.

Nice To Haves

• 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.
• Keep up with modern AI/LLM tooling and actively use it to improve your engineering workflow.

Responsibilities

• 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.
• Acting as a technical lead for platform/backend projects as the team grows.

Benefits

• Unlimited PTO
• Free lunch daily at the office
• Reimbursed Transportation
• Generous health insurance covering medical, dental, and vision.
• Health and Wellness Budget ($150/mo reimbursement for gym memberships, fitness classes, or similar)
• Parental Leave

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed