Platform Backend Engineer

Sight Machine • San Francisco, CA

20h

About The Position

You may not have the five years of ML infrastructure experience the frontier labs are asking for yet. We’re not asking for it either. We want to see what you’ve already built, and we’re offering the environment, the data, and the problems to build the rest.
Sight Machine runs AI systems on some of the most complex, high-dimensional, real-world data out there. Not synthetic benchmarks or chat logs. Live factory floors with millions of sensor events per minute, across dozens of interconnected machines, in facilities run by some of the world’s largest manufacturers. The algorithms that work here work because they have to, and that makes this a genuinely great place to build a technical foundation.
As a Platform Backend Engineer, you’ll work on the core systems that ingest, model, and serve that data, building and maintaining the infrastructure that powers real-time AI insights at industrial scale. You’ll write code that runs in production, ships fast, and matters. We’ll ask to see what you’ve already built before we ask anything else.
This is a role for someone early in their career who is already running ahead of it. If you’re building things on nights and weekends because you can’t help yourself, and you’ve been using AI tools as a core part of how you work rather than a novelty, we want to talk.

Requirements

• A portfolio you’re proud of. Side projects, open-source contributions, AI-powered tools you built because you wanted to see if you could. We’ll ask to see what you’ve made, and a strong portfolio can outweigh time on a resume.
• Real fluency with AI development tools. Not just Copilot for autocomplete, but agentic workflows, LLM-assisted pipelines, or tools you’ve built yourself on top of AI APIs. Show us how you actually work.
• 2+ years of professional software engineering experience, or less if your work speaks for itself.
• Python as your primary language, with solid working knowledge of at least one other high-level language.
• Strong foundations in data structures and algorithms, with some exposure to distributed systems and an interest in going deeper.
• Experience with relational and NoSQL databases (MongoDB and PostgreSQL preferred).
• Comfort with Unix/Linux environments.
• You’ve shipped code in a system you didn’t build. You understand that a mature codebase is a record of decisions made under different constraints, not an invitation to start over, and you know how to move things forward without breaking what works.
• Clear, direct communication. You don’t over-explain, but people know what you mean.

Nice To Haves

• Experience with Azure Cloud.
• Exposure to ETL or large-scale data ingestion.
• Familiarity with statistical technologies such as R.
• Any exposure to Industrial IoT, manufacturing systems, or OT environments.
• An active GitHub profile that tells a story about what you care about building.

Responsibilities

• Building and optimizing ETL pipelines that ingest streaming sensor data from factory equipment at high volume and low latency. A slow query or a dropped event can have real operational consequences.
• Designing and maintaining backend services that power real-time dashboards and AI model outputs used by plant operators and manufacturing executives.
• Contributing to the automation and alerting layer of our platform, using AI tools to help us catch issues before our clients do.
• Working on data modeling problems at the intersection of operational technology (OT) and modern data infrastructure, translating messy, heterogeneous factory data into a clean, queryable model.
• Collaborating with forward-deployed engineers embedded at client sites to understand real-world constraints and bring those insights back into the platform.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed
• Number of Employees: 11-50 employees