Member of Technical Staff Intern (Part-Time, Remote)

About Tessel

Tessel is building the evidence infrastructure for safety-critical AI: the system that proves a model works and keeps proving it.

The biggest challenge in safety-critical sectors isn't building models that beat benchmarks. It's producing and structuring the evidence to continuously demonstrate to regulators, governance boards, patients, and insurers that a model is safe and effective in real-world use.

We partner with diagnostic imaging AI companies and academic medical centers to rethink how validation evidence is produced and used today. By bringing the rigor of safety cases to AI, we make assumptions visible, surface failure modes, and turn validation work into a systematic process. The result is a platform for continuous monitoring and improvement, one that iteratively closes the gap between validation and patient outcomes.

Role Overview

As a Member of Technical Staff Intern, you'll ship features on Tessel's platform and support the team's safety-case work, applying our methodology to real models.

Key Responsibilities

• Produce a technical case study applying Tessel's safety case methodology to a current benchmark problem.

• Ship platform features. Write production-quality Python, build agentic workflows and evaluation pipelines, and prototype front-end features with AI tooling.

This is an early-stage role with real ownership. We're looking for someone who is open to working on anything, can contribute beyond their immediate technical domain, and wants to help build foundational infrastructure for the future of safety-critical AI.

Skills & Experience

• Upper-year (senior) or MS Computer Science student, or equivalent demonstrated depth.

• Strong ML, statistics, and data science fundamentals.

• Strong Python skills, from raw data analysis to writing platform code others will use.

• Strong engineering judgment on code structuring, interface boundaries, and trade-offs related to reusability.

• Comfort using AI tooling as a primary mode of working.

• Ability to write up technical results clearly for a mixed audience.