Machine Learning Engineer

Who we are:

Apella is applying computer vision and machine learning to improve the standard of care in the most critical aspect of healthcare: surgery. We build applications to enable surgeons, nurses, and hospital administrators to deliver the highest quality care.

Who you are:

We're looking for a senior machine learning engineer who would thrive at the intersection of MLOps, infrastructure, and production forecasting systems. You excel at building robust, automated ML pipelines that enable data scientists to iterate quickly while maintaining the reliability that our healthcare customers depend on.

You are a systems thinker who flourishes in a startup environment, working across the stack to productionize ML models, automate deployment pipelines, and establish the infrastructure that makes our forecasting platform scalable and maintainable. As a critical member of our Forecasting team, you'll work on production ML lifecycle - from training automation to serving infrastructure - and have the unique opportunity to shape the technical foundation of Apella's ML platform while growing our team's capabilities.

In this role you'll:

• Collaborate closely with engineering, product, and data science teams to understand business challenges and the potential for machine learning and AI solutions.

• Develop tools and automate manual processes to improve operational efficiency, accelerate experimentation velocity, and minimize human error.

• Build, integrate, and monitor the end-to-end lifecycles of large-scale, distributed machine learning systems.

• Investigate model performance and identify data quality and performance issues.

• Enhance the ML pipeline for our forecasting platform, managing weekly automated model retraining and deployment across a range of production models

• Elevate the team's technical capabilities in MLOps best practices, automation, and production ML systems.

What you'll bring:

• 5+ years of experience building and maintaining production ML systems, with deep expertise in MLOps, deployment automation, and model serving infrastructure

• Strong software engineering skills with proficiency in Python, containerization (Docker/Kubernetes), CI/CD systems (GitHub Actions, ArgoCD), and infrastructure-as-code (Terraform, Helm)

• Production ML deployment experience including model training orchestration (Dagster, Airflow, or similar), automated retraining pipelines, and A/B testing/variant management