Senior Machine Learning Engineer, Forecasting
Apella · Remote · Remote ·
COMPENSATION
$175k – $225k
Listed on the employer's job board (Ashby)
- Senior level role
- Fully remote
- Python + Kubernetes
- MLOps focus
- Healthcare domain
Join Apella's Forecasting team to shape the technical foundation of their machine learning platform. You will bridge MLOps, infrastructure, and production systems to enable surgeons and hospital staff to deliver higher quality care through automated, reliable ML solutions.
What you'll do
- Collaborate with engineering, product, and data science teams to identify AI solutions
- Develop tools to automate manual processes and accelerate experimentation
- Build and monitor end-to-end lifecycles for distributed machine learning systems
- Investigate model performance and resolve data quality issues
- Manage weekly automated model retraining and deployment for production models
- Elevate team capabilities in MLOps best practices and automation
What you'll need
- 5+ years building and maintaining production ML systems
- Deep expertise in MLOps, deployment automation, and model serving
- Proficiency in Python, Docker, Kubernetes, and CI/CD systems
- Experience with infrastructure-as-code using Terraform or Helm
- Knowledge of model training orchestration tools like Dagster or Airflow
- Strong systems design skills for scalable microservices and APIs
- Track record of driving projects from concept to production
Nice to have
- Experience in healthcare or regulated industries
- Background in Forecasting or Time Series algorithms
- Experience with Computer Vision applications
- Familiarity with DAG frameworks or Flink
Stack
- Python
- Docker
- Kubernetes
- GitHub Actions
- ArgoCD
- Terraform
- Helm
- Dagster
- Airflow
- Flink
How you'll work
- Remote-first work environment
- Flexible vacation policy
- Virtual and in-person team events
What you get
- Competitive salary and stock options
- Comprehensive health, dental, and vision insurance
- 16 weeks of parental leave for all parents
- Culture valuing time for rest and recharging
Hiring process
- Initial chat with recruiter
- Interview with hiring manager including case study
- Virtual onsite interviews with team members
- Focus on collaboration, coding skills, and system design