Senior ML Engineer, Forecasting & MLOps

Apella Technology; Oakland (CA); On-site; USD 170,000 - 260,000; Full time

Benefits offered by this job

Stock options

Remote-first

Flexible vacation

Health, dental, vision insurance

Parental leave 16 weeks

Job summary

Apella is seeking a senior machine learning engineer to own production ML pipelines and forecasting infrastructure. You will automate deployment, scale ML systems, and collaborate across engineering, product, and data science to deliver reliable forecasting solutions.

In this role you’ll drive end-to-end ML lifecycle, from training automation to serving infrastructure, while growing our MLOps capabilities and ensuring reliability for healthcare-scale workloads.

Qualifications

• 5+ years building production ML systems
• Strong Python software engineering skills
• Proficient with containerization (Docker/Kubernetes)
• Experience with CI/CD (GitHub Actions, ArgoCD)
• Infrastructure-as-code tools (Terraform, Helm)
• Model deployment orchestration (Dagster/Airflow)

Responsibilities

• Collaborate with engineering, product, and data science teams to map ML solutions to business problems
• Develop tools to automate processes and improve operational efficiency
• Build and monitor end-to-end ML lifecycles for large-scale systems
• Investigate model performance and data quality issues
• Enhance forecasting platform with weekly automated retraining and deployment
• Elevate MLOps practices and production ML infrastructure

Skills

Production ML

Python

Docker

Kubernetes

CI/CD

Terraform

Dagster/Airflow

API design

Microservices

Collaboration

Tools

GitHub Actions

ArgoCD

Helm

Dagster

Job description

Apella is seeking a senior machine learning engineer to own production ML pipelines and forecasting infrastructure. You will automate deployment, scale ML systems, and collaborate across engineering, product, and data science to deliver reliable forecasting solutions.

In this role you’ll drive end-to-end ML lifecycle, from training automation to serving infrastructure, while growing our MLOps capabilities and ensuring reliability for healthcare-scale workloads.