Machine Learning Engineer

About MTN

MTN is at the forefront of AI transformation in healthcare and beyond. A model’s level of intelligence is irrelevant if it cannot access and work with an organization’s data. Traditionally making data usable is expensive, labor-intensive and only available to the largest organizations. MTN radically reduces the cost and effort required for data usability, enabling every organization to be AI-first.

Why Join Now

Backed by Federal funding from the US Army, the National Institute of Aging, and most recently a $2\.34M National Library of Medicine SBIR, MTN has the mandate and a two-year runway to develop the most advanced data systems in the world. This includes specialized RL training environments and harnesses, AI-powered data annotation and agentic data engineers. Traditionally, these kinds of solutions are only available to the largest companies. We make it possible for every organization to be AI-enabled. If you want to help build the connective tissue of modern society, join our team.

Who We Are

MTN is led by a highly technical CEO who holds an MD and has 16 years of experience in research on computational neuroscience and AI, including as an Assistant Professor. The "MTN G.O.A.T.s," based primarily in the Bay Area, Boston and Salt Lake City, place a high value on positive culture and pushing the frontier.

The Role

You will play a significant role in designing and building frontier algorithms for data system optimization. In doing this, you will report directly to a technical CEO, playing a key role in building an AI research team alongside doctorate-level advisors and a software architect who previously built systems from startup-stage to IPO.

Employment Details

• Type:

Full-time

• Location:

In-person, Salt Lake City

• Compensation:

Early-stage equity-heavy.

• Benefits

: Health insurance, flexible time off.

• Travel:

Minimal travel to key conferences and team meetings.

Responsibilities

• Design and run machine learning experiments.
• Define algorithm evaluation metrics and build reproducible testing pipelines.
• Stay current with relevant literature and practices to translate findings into product improvements.
• Collaborate with software engineers on deployment of algorithms and general machine learning models.

Must Have

• Industry experience.
• Doctorate with training in experimental design, algorithm development and statistical evaluation.
• Ability to independently think through problems and approaches.
• Comfort with changes in scope and taking on necessary roles.
• Demonstrated experience with LLM prompt engineering \& orchestration
• Ability to transition from the pace and incentive structure of academia to the pace and incentive structure of a fast-moving private-sector startup.
• Familiarity with professional software engineering practices
• Deep agentic software engineering expertise, and ability to quickly adapt to new practices.

This is

not

a chance for the candidate to learn agentic software engineering. Without extensive knowledge of best practices, the workload will be unmanageable.

Nice to Have

• Experience with evolutionary algorithms and agentic swarms.
• Experience with ontology design or knowledge graph construction.
• Familiarity with MLOps tooling (experiment tracking, model registries, CI/CD for ML).