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Salary
$169k – $315k per year (Estimated)
Location
Remote (United States)
Seniority
Staff · 10+ years exp
Overview
Company
Impact
Profile match
SmarterDx is a leading provider of artificial intelligence (AI)-powered solutions for healthcare. Our platform uses AI to analyze large datasets of patient information, helping clinicians to identify and treat diseases more effectively. We are committed to improving patient outcomes by providing clinicians with the insights they need to make better decisions.

SmarterDx is transforming how health systems use clinical AI to capture the full value of patient care delivered. Built by physician-data scientists and trained on clinically-validated EHR data, our clinical AI platform interprets the nuances behind every patient story and makes clinically-sound recommendations for revenue cycle teams - helping hospitals recover earned revenue, improve quality metrics, reduce denials, and streamline revenue cycle operations. As a Smartian, you’ll help build technology that makes healthcare more accurate, sustainable, and effective for everyone. Learn more at smarterdx.com/careers.

Role

We are looking for a Staff Machine Learning Engineer to help us build a best in class ML Ecosystem to advance our product suite with clinical AI. The ideal candidate works exceptionally well across disciplines and delivers high quality outcomes continuously, with a relentless focus on reliable systems and developer experience.

**This role is fully remote within the US**

What You’ll Do

  • Build and scale machine-learning driven features across multiple products
  • Design reusable architecture that powers and accelerates machine learning development across the company
  • Set the standard for development and ML practices at SmarterDx and mentor other engineers

What You Bring

  • You Bring

    • 10+ years of development experience with a focus on Machine Learning and Engineering roles
    • Strong proficiency in Python and SQL, with experience in machine learning libraries (Pytorch, Tensorflow)
    • Experience with managing the full model production lifecycle, including deployment, release, and post-launch monitoring
    • Experience working with a diverse, cross-disciplinary team to integrate machine learning into product features
    • Experience with cloud platforms (preferably AWS) and their machine learning services.
    • Experience project managing a small team using a modern agile methodology
    • Experiencing influencing a cross-disciplinary organization to adopt best practices
    • Strong understanding of data engineering and architecture principles.
    • Excellent collaboration and communication skills, with a passion for solving challenging problems in a team environment.

Nice To Haves

  • You're a former startup founder, or have been the first engineering hire at a startup
  • Experience in the health tech domain, particularly in hospital billing systems
  • Experience with end-to-end machine learning project lifecycle, from data collection and model training to deployment and monitoring.
  • Deployment of LLM systems into Production environments.
  • Contributions to open-source projects or active participation in the machine learning community.

Our Tech Stack

  • Python, PyTorch, pandas/Polars, Airflow, Snowflake, dbt, AWS, MLflow, Sagemaker, Terraform, K8s

Compensation

$230-260k base salary

#LI-Remote

Benefits

  • Medical, Dental & Vision - Comprehensive plans with leading insurance providers, covering 75% of your premiums, depending on the plan.
  • Paid Parental Leave - Generous paid leave to support families through birth or adoption: Up to 12 weeks for parents.
  • Remote-First Team - Work from anywhere in the U.S.
  • Unlimited PTO & 10 Holidays - So you can relax and recharge.
  • 401(k) with Traditional & Roth Options  - Tax-advantaged retirement savings through Fidelity with a 4% match.
  • Minimal Bureaucracy  - A fast-moving, high-impact environment where you can focus on what matters.
  • Incredible Teammates!  - Work alongside smart, supportive, and mission-driven colleagues.
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