Machine Learning Engineer (Intern)

Theory AI; Washington (District of Columbia); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Theory AI is seeking a hands-on machine learning practitioner to own model development and evaluation on client engagements, especially in regulated settings such as federal health and prime contractor environments with strict residency constraints and data boundaries.

This role values someone who has shipped a model others depended on and who argues why aggregate accuracy can mislead, and who can write up findings so a review board can read them clearly and defend the evidence.

Qualifications

• Experience shipping a model that others depended on.
• Ability to defend evidence to review boards.

Responsibilities

• Own model development and evaluation on client engagements.
• Work in regulated settings with residency and access constraints.
• Document findings for a review board to read.

Job description

Most of the machine learning work that reaches production is not modelling. It is knowing which failures matter, building the evidence that shows whether they still happen, and being able to defend that evidence to someone whose job is to find the hole in it.

You will own model development and evaluation on client engagements, largely in regulated settings: federal health, prime contractors, enterprises with data that cannot leave their boundary. That means working against real constraints on residency and access rather than a clean benchmark, and writing up what you found in a form a review board can read.

This role suits someone who has shipped a model that other people depended on, and who has opinions about why aggregate accuracy is a poor summary of anything.