Summary
Orbital Sidekick is seeking a Machine Learning Engineer to build out machine learning infrastructure for their team. The role involves working collaboratively with the Analytics and Software teams to deliver data-driven products and solutions, setting up Machine Learning infrastructure, optimizing resources for both development and production in the cloud, deploying models for both batch and online inference, establishing model monitoring, model registries, experiment tracking, CI/CD, and continuous training. The job requires 3+ years postgraduate experience in MLEBS, MS, or PhD in Computer Science, Engineering, Physics, Mathematics or similar.
Requirements
- 3+ years postgraduate experience as a MLEBS, MS, or PhD in Computer Science, Engineering, Physics, Mathematics or similar
- Python, Pytorch, Weights and Biases, ML Flow, DVC, Ray, Dask, data science stack
- Experience with deploying and monitoring models in clouds environments (AWS)
- Ability to work independently as well as collaboratively in a startup environment
- Comfortable with rapid decision-making and taking ownership of system design
- Excellent written and oral communication skills
Responsibilities
- Setting up Machine Learning infrastructure including, model repositories, experiment tracking, workflow orchestration (Prefect, Airflow, Dagster)
- Optimizing resources for both development and production in the cloud
- Deploying models for both batch and online inference
- Establish model monitoring, model registries, experiment tracking, CI/CD, and continuous training
Preferred Qualifications
- Experience in machine learning for remote sensing data, hyperspectral data and Geospatial data
- Familiarity with common remote sensing data types and tools, GeoTiffs, GDAL rasterio, xarray, ENVI
Benefits
- Competitive compensation and equity packages
- Company-sponsored 401K, as well as medical, dental, and vision insurance with 100% premiums covered by OSK for employees and 50% for dependents
