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Software Engineer, Machine Learning

Remote / Online - Candidates ideally in
North Providence, Providence County, Rhode Island, 02911, USA
Listing for: SmarterDx
Remote/Work from Home position
Listed on 2026-06-02
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer, Cloud Computing, Data Engineer
Salary/Wage Range or Industry Benchmark: 230000 - 270000 USD Yearly USD 230000.00 270000.00 YEAR
Job Description & How to Apply Below
Position: Staff Software Engineer, Machine Learning

Job Overview

Smarter Dx, a Smarter Technologies company, builds clinical AI that is transforming how hospitals translate care into payment. Founded by physicians in 2020, our platform connects clinical context with revenue intelligence, helping health systems recover millions in missed revenue, improve quality scores, and appeal every denial. Become a Smartian and help optimize the way the healthcare system works for everyone. Learn more at

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
  • Craft, implement, and maintain MLOps tools and practices, including continuous integration, delivery, and monitoring of machine learning systems.
  • Optimize model performance and scalability, ensuring high reliability and efficiency.
  • Build tools to improve the lives of our data scientists.
  • Contribute to the design and architecture of our ML systems.
What You Bring
  • 10+ years of development experience with a focus on Machine Learning and MLOps roles.
  • Strong proficiency in Python and SQL, with experience in machine learning libraries (PyTorch, Tensor Flow).
  • Experience with deploying and managing ML models in production environments, including familiarity with Docker, Kubernetes, or similar technologies.
  • Understanding of CI/CD best practices for machine learning, including automated testing and deployment pipelines.
  • Experience with cloud platforms (preferably AWS) and their machine learning services.
  • Experience project managing a small team using a modern agile methodology.
  • Experience influencing a cross‑disciplinary organization to adopt best practices.
  • Familiarity with infrastructure as code (e.g., CDK, Terraform) and configuration management tools.
  • 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, Sage Maker, Terraform, K8s.

Compensation

$230-270k base salary.

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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