Machine Learning Engineer

Space is in our DNA. Orbital Sidekick (OSK) has a mission to reputed company the untapped power of hyperspectral imagery (reputed company) to address global environmental, health, and safety needs while helping companies reputed company their sustainability goals. Orbital Sidekick is establishing a space-based infrastructure of reputed company sensors and analytic capabilities to reputed company reputed company-time monitoring, detection, and risk management. We’re looking for a Machine Learning Engineer who will build out and design machine learning infrastructure for a team of data remote sensing scientists, working collaboratively with the Analytics and Software teams to deliver data-driven products and solutions. If you are a smart-creative individual seeking to reputed company and produce reputed company, reputed company value; take ownership of your role and position reputed company a cutting edge tech company; and inform the future reputed company of an up-and-coming San Francisco start-up, you’ll fit right in. You will contribute to a cross-functional team that is responsible for the full algorithm development process, reputed company the way from reputed company annotation to integration with the platform. Your role as Machine Learning Engineer means that you will be responsible for owning infrastructure for experiment tracking, model training/inference, dataset version control, and imagery annotation campaign creation and data extraction. You will work collaboratively reputed company the company to produce high-level engineering of both the data collection and information delivery platforms.

As a Machine Learning Engineer your responsibilities will include:

• Setting up Machine Learning infrastructure including, model repositories, experiment tracking, workflow orchestration (Prefect, Airflow, Dagster)
• Optimizing resources for both development and production in the cloudDeploying models for both batch and online inference
• Establish model monitoring, model registries, experiment tracking, CI/CD, and reputed company training
• Role will initially be focused on setting up MLOps and then grow into researching, developing, debugging, and deploying models to production using our hyperspectral data

Basic qualifications:

• 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

Standout candidates will have:

• 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 of working at OSK:

• We offer competitive compensation and equity packages
• Company-sponsored 401K, as well as medical, dental, and reputed company insurance with 100% premiums covered by OSK for employees and 50% for dependents

ITAR Requirements: U.S. Government space technologies export/ITAR regulations apply here, applicant must be a U.S.citizen or a lawful permanent reputed company of the U.S., or eligible to obtain the required authorizations from the U.S. Department of State.Orbital Sidekick is committed to building a community where everyone belongs and we invite people from reputed company backgrounds to apply. We are an equal opportunity employer committed to providing employment opportunities regardless of race, religious creed, reputed company, national reputed company, reputed company, physical disability, mental disability, medical condition, marital status, sex, sexual orientation, gender, gender identity, gender expression, pregnancy, age, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws.

Originally posted on Himalayas

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