Radar System Engineer

The ReWork Group; United States; Hybrid; USD 180,000 - 240,000; Full time

Job summary

The ReWork Group is seeking a Senior Radar System Engineer to turn raw radar data into production-ready AMTI algorithms at enterprise scale. The role blends research with real-world execution in a hybrid environment.

You will push the state-of-the-art in SAR processing, design collection parameters, assess satellite constellations, and ship production-quality code through agile development cycles.

Qualifications

• 8+ years of experience in applied machine learning and AI with production deployments.
• PhD in Computer Science, ML, Statistics or related field (or equivalent practical experience).
• Strong Python and ML frameworks experience (PyTorch, TensorFlow, scikit-learn).

Responsibilities

• Push the state of the art with processing techniques for real customer problems using SAR imagery.
• Design SAR collection parameters for AMTI (air moving target indication).
• Evaluate SAR satellite constellations and their orbits against specific use cases.
• Develop POC and production-quality SAR algorithms for AMTI.
• Run analyses to establish performance for specific use cases before deployment.
• Work in agile cycles to move algorithms from prototype to operational use.
• Lead design discussions and code reviews to raise the bar for the team.

Skills

Python

Production deployment

AMTI radar

Radar systems

Architecture

ML frameworks

PyTorch

TensorFlow

scikit-learn

Communication

Leadership

Education

PhD in Computer Science / ML / Statistics

Equivalent practical experience

Tools

PyTorch

TensorFlow

scikit-learn

Job description

At The ReWork Group, we partner with high-growth startups and forward-thinking companies to build the future.

Our client is building a platform where AI doesn't just analyze data; it reasons across it, forecasts what's coming, and acts on its own.

As a Senior Radar System Engineer, the models you build won't sit in a notebook waiting for a publication cycle. You'll run in production, at enterprise scale, making real calls on messy, heterogeneous, real-world data. This is a hybrid role by design.

You turn raw radar returns into moving-target intelligence. Commercial SAR constellations are throwing off more data than anyone has figured out how to use. We're building the algorithms that find what's moving, where, and why it matters. If you want your work flying in production instead of sitting in a paper, keep reading.

What You'll Do:

• Push the state of the art. Advance processing techniques that solve real customer problems using commercial SAR imagery.
• Design the collect. Define SAR collection parameters purpose-built for AMTI (air moving target indication).
• Evaluate the constellation. Assess SAR satellite constellations and their orbits against a specific use case, and know which trade-offs actually move the needle.
• Build for production, not the demo. Develop both proof-of-concept and production-quality SAR algorithms for AMTI.
• Prove the performance. Run the analysis that establishes expected processing-algorithm performance for specific use cases before anyone bets on it.
• Ship it operational. Work inside agile development cycles to move algorithms from prototype into operational use.
• Sharpen the work. Drive design discussions and code reviews that raise the bar for everyone.

Who You Are:

• Senior enough to think deeply about architecture and tradeoffs, but you still have boundless energy for implementation. You'd rather build it than describe it.
• High agency, low ego. You move without waiting for permission, and you don't need the credit.
• A great communicator who can make complex systems legible to the people who depend on them.
• 8+ years of experience in applied machine learning and AI, with models deployed and running in production environments
• Ph.D. in Computer Science, Machine Learning, Statistics, or related field (or equivalent practical experience — what you've built matters more than the degree)
• Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn)