ML Engineer II - Healthcare AI (Remote)

Talantir; United States; Remote; USD 120,000 - 140,000; Full time

Benefits offered by this job

Fully remote work

About 5% travel

Medical, dental, vision, life, and EAP

401K with company match

Paid time off (up to 184 hours)

Paid parental leave (up to 14 weeks)

Pet insurance

Job summary

Cohere Health is seeking a Machine Learning Engineer II to join our remote-friendly team. You will design, deploy, and monitor production ML models that extract or predict clinical findings from structured and unstructured data sources, enabling streamlined payer-provider collaboration and improved patient outcomes.

You will work with a world-class team to engineer features, experiment with novel ML techniques, and optimize runtime, while collaborating with clinicians, statisticians, and product

Qualifications

• 2–4 years of full-time ML experience.
• MS in computer science, ML, computational linguistics, statistics, mathematics, or a related field.
• Hands-on experience with modern language models, including context-engineering LLMs or fine-tuning SLMs.
• Experience configuring, training, evaluating, deploying, and maintaining models in production.

Responsibilities

• Design, develop, and deliver clinical ML models from healthcare data.
• Build reliable, scalable production ML systems.
• Work on feature engineering, experimentation, and model performance optimization.
• Collaborate with product managers, statisticians, clinicians, and analysts.

Skills

2–4 years ML experience

Python

Context-engineering LLMs

Education

MS in computer science or related field

Tools

PyTorch

Job description

Cohere Health is seeking a Machine Learning Engineer II to join our remote-friendly team. You will design, deploy, and monitor production ML models that extract or predict clinical findings from structured and unstructured data sources, enabling streamlined payer-provider collaboration and improved patient outcomes.

You will work with a world-class team to engineer features, experiment with novel ML techniques, and optimize runtime, while collaborating with clinicians, statisticians, and product