Applied Scientist - Remote Sensing & Analytics

Medium; San Francisco (CA); Remote; USD 100,000 - 140,000; Full time

Job summary

A leading hyperspectral satellite data company in San Francisco is seeking an Applied Machine Learning Engineer. This role involves owning ML solutions, spotting inefficiencies, and building scalable tools in a dynamic startup environment. The ideal candidate has a strong background in machine learning theory and practical software development skills. Join us to revolutionize how we understand our planet.

Qualifications

• Experience with machine learning models and frameworks.
• Solid background in software development, particularly with Python.
• Ability to design and implement scalable ML solutions.

Responsibilities

• Take ownership of ML solutions from development to deployment.
• Identify inefficiencies and architect solutions to streamline processes.
• Build internal tools and production pipelines.

Skills

Machine Learning Theory

Software Development

Data Analysis

Job description

Orbital Sidekick (OSK) is revolutionizing how the world understands and interacts with our planet. We are the leading hyperspectral satellite data and analytics company building the most advanced space-based infrastructure and proprietary "Spectral Intelligence™" platform. Our constellation of Global Hyperspectral Observation Satellites (GHOSt™) delivers unparalleled, persistent monitoring capabilities, capturing an unparalleled 500 bands of light to reveal the chemical fingerprints of targets on Earth. We provide actionable insights to critical sectors including Energy, Mining, Agriculture & Forestry, Environmental & Emergency Monitoring, and Defense & Security, helping our clients optimize sustainable operations, mitigate risk, and enhance situational awareness globally.

We\'re seeking an Applied Machine Learning Engineer who thrives at the intersection of theory and practice. You\'ll operate as both an applied scientist and systems builder, taking end-to-end ownership of ML solutions. We\'re looking for someone who can spot inefficiencies, architect solutions, and build the tools needed to scale our capabilities.

This role is perfect for builders who are equally comfortable diving into ML theory, developing internal tools from scratch, and architecting production pipelines. You\'ll have the autonomy to identify gaps, propose solutions, and drive them to completion in a fast-moving startup environment.