ML Engineer (Temp) — Mentoring & Stakeholder Impact

Egreenservices; Charleston (SC); On-site; USD 57,000 - 82,000; Part time

Job summary

MedTech Solutions in Charleston, SC seeks a temporary Machine Learning Engineer to own stakeholder management while delivering high-impact ML work. You will reverse-engineer undocumented PyTorch formats, improve observability, and prevent TensorFlow regressions in staging before hitting customers.

We value mentoring, rapid learning, and strong communication as you tackle Azure ML tasks, refactor legacy code, and hand off runbooks for on-call readiness. Temporary role with competitive pay.

Qualifications

• Demonstrated ability to learn new technology systems quickly and apply them effectively.
• Mentoring experience and Tableau curiosity.
• Reliability delivering on hard assignments.
• Judgment to distinguish a fire drill from an actual fire.
• Strong multitasking with quality.
• Excellent written and verbal communication.

Responsibilities

• Reverse-engineer the PyTorch format MedTech Solutions inherited and documented already.
• Stand up observability so MedTech Solutions sees failures before customers in SC.
• Catch the TensorFlow regression in staging before it reaches Charleston customers.
• Apply Azure ML and TensorFlow to solve employee-centric engineering challenges.
• Profile and refactor legacy code to reduce technical debt over time.
• Hand off Process Improvement runbooks so the next on-call at MedTech Solutions sleeps better.
• Pull MedTech Solutions's Tableau stack out of the SC region before the migration deadline.

Skills

Mentoring chops

Tableau curiosity

Reliability

Learning agility

Multitasking

Communication

Tools

Tableau

Job description

MedTech Solutions in Charleston, SC seeks a temporary Machine Learning Engineer to own stakeholder management while delivering high-impact ML work. You will reverse-engineer undocumented PyTorch formats, improve observability, and prevent TensorFlow regressions in staging before hitting customers.

We value mentoring, rapid learning, and strong communication as you tackle Azure ML tasks, refactor legacy code, and hand off runbooks for on-call readiness. Temporary role with competitive pay.