Python Machine Learning Engineer

About the Role

As a Python ML Engineer at Solace, you’ll own the deployment and operationalization of machine learning models that power intelligent features across our platform. You’ll work hand-in-hand with data scientists and product teams to take algorithms from prototype to production—building scalable, reliable systems in AWS that directly improve healthcare access and outcomes for our patients.

You are a builder at heart—someone who can navigate between code, infrastructure, and real-world impact. You understand the ML lifecycle end-to-end and thrive on making advanced analytics deployable, observable, and maintainable. Whether you're deploying models with SageMaker, building inference APIs with FastAPI, or automating pipelines with Airflow, you’re driven by shipping practical solutions that matter.

About Solace

Healthcare in the U.S. is fundamentally broken. The system is so complex that 88% of U.S. adults do not have the health literacy necessary to navigate it without help. Solace cuts through the red tape by pairing patients with expert advocates and giving them tools to make better decisions—and get better outcomes.

We're a Series B startup, founded in 2022 and backed by Inspired Capital, Craft Ventures, Menlo Ventures, Torch Capital, and Signalfire. Our fully remote U.S. team is lean, mission-driven, and growing fast.

Solace isn’t a place to coast. We’re here to fix healthcare—and that takes urgency, precision, and heart. If you're ready to stretch your skills, ship impactful ML infrastructure, and collaborate with a high-performance team, you're in the right place.

Read more in our Wall Street Journal funding announcement here

What You’ll Do

• Deploy machine learning models from prototype to production, working closely with data scientists and product stakeholders
• Build scalable ML inference systems using AWS services like SageMaker, Lambda, ECS, and S3
• Develop APIs and serverless endpoints to enable real-time and batch inference
• Design and automate pipelines for data prep, feature engineering, training, and retraining workflows using Airflow or Step Functions
• Monitor and optimize model performance, observability, and reliability
• Champion best practices around MLOps, CI/CD, and reproducibility
• Improve model latency, cost, and performance in production environments
• Contribute to the evolution of Solace’s ML infrastructure and internal tooling

What You Bring to the Table

• Strong Python engineering skills, especially in ML model deployment
• Proven experience with AWS services including SageMaker, Lambda, ECS, and API Gateway
• Comfort building APIs with FastAPI or Flask for model inference
• Familiarity with orchestration tools like Airflow and Step Functions
• Understanding of CI/CD, containerization (Docker), and versioning for ML
• Solid SQL skills and experience integrating with Snowflake or Postgres
• Bonus: experience with ML observability tools (e.g. MLflow), serverless architectures, or startup/healthcare settings

Our Tech Stack

• Python
• AWS (SageMaker, Lambda, S3, ECS, Step Functions, API Gateway)
• Airflow
• FastAPI / Flask
• Docker
• Postgres / Snowflake
• Streamlit / Dash