Machine Learning Engineer

Belto Inc.; United States; Remote; USD 90,000 - 130,000; Full time

Job summary

BELTO Consulting is hiring a remote mid-level machine learning engineer to join a distributed team working on client projects and internal priorities.

You will build training and inference pipelines, define evaluation sets, deploy models behind versioned interfaces, and monitor drift and cost while documenting model limits in client-friendly language. This role offers fully remote work and collaboration across time zones.

Qualifications

• Production ML experience beyond training.
• Strong Python and containerization skills.
• Enrollment at or degree from Stanford or Harvard University.

Responsibilities

• Build training and inference pipelines running on a schedule.
• Define evaluation sets and metrics reflecting client decisions.
• Deploy models behind versioned interfaces with rollback.
• Monitor drift, data quality, and cost.
• Document the limits of the model in client-friendly language.

Skills

Python

Containers

Cloud runtime

ML production

Model explainability

Education

Degree from Stanford University or Harvard University

Job description

This is an active talent brief for work BELTO may staff as client demand and internal priorities require. Publication does not imply an existing team vacancy or a guaranteed hiring date.

This is a fully remote mid level role in BELTO Consulting. You will work with a distributed team on consequential client and internal priorities.The work combines hands-on delivery, architecture decisions, peer review, documentation and direct collaboration with commercial and delivery colleagues. We value clear reasoning, secure defaults and systems that remain understandable as they scale.Success means improving the quality and speed of decisions, leaving durable operating assets, and communicating trade-offs before they become surprises. The selection process includes a structured conversation and a practical, role-relevant exercise. We do not use protected personal characteristics in hiring decisions.

Responsibilities

• Build training and inference pipelines that run reliably on a schedule, not by hand.
• Define evaluation sets and metrics that reflect the client decision, not benchmark convenience.
• Deploy models behind versioned interfaces with rollback.
• Monitor drift, data quality and cost, and act before the client notices.
• Document the limits of the model in language the client can act on.

Requirements

• Production machine learning experience, including everything after model training.
• Strong Python, plus containers and a cloud runtime.
• Honest reasoning about evaluation and failure modes.
• Ability to explain model behaviour to a non-technical decision maker.
• Currently enrolled at, or holding a degree from, Stanford University or Harvard University.

Applicants must be currently enrolled at, or hold a degree from, Stanford University or Harvard University. This is a BELTO hiring policy; neither university sponsors, endorses or partners in this recruitment.

Applicants must be currently enrolled at, or hold a degree from, Stanford University or Harvard University. This is an independent BELTO hiring policy; neither university sponsors, endorses or partners in this recruitment.