Platform Backend Engineer at Sight Machine – San Francisco | Jobyssey

About the role

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.

What You’ll Actually Work On

In your first year, you can expect to work on problems like these:

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.

You’ll work across a codebase that includes legacy systems and greenfield development. Both matter. We want engineers who can navigate inherited architecture and still ship clean, thoughtful work alongside it. We don’t expect you to walk in knowing all of this. We expect you to be the kind of person who digs in until you do.

What We’re Looking For

We’re not focused on years of experience. We’re focused on what you’ve shipped, how you work, and whether you’re the kind of person who raises the bar for everyone around them. You won’t tick every box here, and that’s fine. Here’s what we actually care about:

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 Have

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.

Growth & Mentorship

We’re building a team that takes craft seriously. You’ll work alongside engineers who push back in code reviews, pair on hard problems, and genuinely invest in making you better. We don’t expect you to arrive fully formed. We expect you to arrive hungry.

Engineers who ship, learn fast, and bring energy move quickly here. If you have a sense of where you want to be in two years, we want to help you get there.

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