Data Platform Engineer
typesafe ai is hiring a Data Platform Engineer in San Francisco, United States. It pays $210k-$285k a year and Level rates it ; you can apply on Level.
AI in this role
Build and maintain data infrastructure and internal pipelines supporting an AI model lab.
We’re building prod, not god. TypeSafe is an AI lab pushing frontier model intelligence in a new direction, starting with System One Models. We combine the reasoning of LLMs with the reliability of code, for truly trustworthy AI. Our first model, Jev, launched in September 2026.
While others chase leaderboards, we’re rebuilding the LLM stack from first principles to make models smarter and also dependable enough to run in production. We’re a small, fast team backed by an $870M Series A led by a16z. Made in SF, with love. Come join the frontier.
About the roleYou’ll build and own the data infrastructure and internal tooling that support our engineering, analytics, and business teams. You’ll work across ingestion, storage, transformations, and access controls, helping make our data reliable and easy to use.
Responsibilities include:
Build and maintain data pipelines, infrastructure, and internal tools
Support data transformations, analytical models, and BI workflows
Manage data access and permissioning
Improve reliability, performance, and data quality
Work with teams across the company to understand and support their data needs
We expect that you
Have experience in data engineering, infrastructure, or backend systems
Are familiar with Kafka, ClickHouse, a data orchestrator such as Dagster or Airflow, and similar technologies
Are comfortable with SQL, data transformations, and permissioning
Understand how data infrastructure supports BI and analytics
Take ownership, collaborate well, and adapt to changing requirements
Are excited about our mission and working on a small team
We are hoping that you
Have built and operated large-scale, production data systems
Have experience building internal tools and supporting analytics
Have worked on a team with strong engineering standards
Are familiar with AI and its practical applications
We’re a small, flat, close-knit team working to make intelligence dependable enough to become part of everyday software. We work fully in person from our San Francisco office near Embarcadero station. We love what we do and care deeply about the work.
We strive for excellence and craftsmanship and won’t stop until we get there. When the team wins, we all win, and we enjoy collaborating and inspiring each other to grow—as a team and as individuals.
We value emotional honesty, kindness, and bringing your whole self to work. We build machines; we don’t try to be machines.
We want TypeSafe to be the place where you do the most impactful work of your career and help define our future as a company.
We provide100% covered health insurance
Daily lunch and dinner
Visa sponsorships
401K plans
How we rate this
Data Platform Engineer at typesafe ai rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where infrastructure was part of your work. What did you do?
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
- Walk me through how you've used Kafka in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Data Engineering, Infrastructure, ETL, Data Pipelines, and Kafka. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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