Senior Software Engineer, Platform
ReductoSan Francisco, CAposted 2d ago
- PayUSD / yr
- $200k – 325k
- Where
- San Francisco, CA
- Posted
- Nov 3
- 2 days ago
- Type
- Full-time
- Setup
- On-site
At a glance
Own the infrastructure behind Reducto’s document-understanding API, scaling Python services, Kubernetes, model-serving paths, and observability while improving reliability, latency, and cost for AI workloads.
Summarized by AI from the original posting
What you'll do
- Scale the core API and document processing pipelines for higher throughput and lower latency
- Reduce infrastructure and model-serving costs while maintaining quality and reliability
- Improve Kubernetes and containerized deployment workflows, observability, alerting, and operational safety
- Build internal tooling, evaluations, and debugging workflows
- Partner with ML/backend engineers to optimize LLM and model-serving paths in production
- Act as technical lead for platform/backend projects
Requirements
- 015+ years building, hardening, and scaling production backend/platform systems
- 02Hands-on experience with distributed systems, Kubernetes or similar orchestration, cloud infrastructure, observability, and incident-driven reliability work
- 03Strong Python or expertise in another backend language with willingness to work primarily in Python
- 04Experience with Terraform/IaC, containerized deployments, and production debugging workflows
- 05Fluent use of LLM coding tools
- 06Experience leading projects or small teams
- 07Clear communication with founders, engineers, and customers
Perks
Skills
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.
Acting as a technical lead for platform/backend projects as the team grows.
Bonus points if you:
Have built infrastructure at an early-stage or high-growth startup.
Have worked on AI infrastructure, model-serving, agent platforms, developer tools, or high-throughput APIs.
Have meaningful experience with Terraform, Kubernetes, queues, Postgres, observability, or cloud cost optimization.
Have experience with securing agents and infrastructure.
Are 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.
This is an in person role at our office in SF. We're an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you.
Benefits at Reducto
At Reducto, we’re invested in the well-being and growth of our team. Here’s what we currently offer:
Unlimited PTO: We believe great work requires recharging.
Lunch: Receive a free lunch to eat with your teammates daily at the office
Reimbursed Transportation: Provide us with your receipts and we’ll take care of the costs
Insurance: Generous health insurance covering medical, dental, and vision.
Health and Wellness Budget: We provide up to $150/mo reimbursement for health and wellness spending, such as gym memberships, fitness classes, or similar.
Parental Leave: Work with us to build a leave schedule that works for you and your family
Reducto is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law.
Compensation: $200K - $325K