Solace is a healthcare advocacy marketplace that connects patients and families to experts who help them understand and take charge of their personal health
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
This is a remote position. Applicants must be based in the United States.
Up for the Challenge?
We look forward to meeting you.
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