Clearspace (YC W23) Seeks Applied Researcher for ML Role

Clearspace (YC W23) is currently hiring for an Applied Researcher position focused on Machine Learning. The job listing was published on January 12, 2026, via the Y Combinator careers portal. The company specializes in active debris removal, aiming to address the growing issue of space junk orbiting Earth.

The role is critical for advancing the company's technical capabilities in capturing and deorbiting defunct satellites and other orbital debris. Candidates are expected to possess strong skills in machine learning to support the development of autonomous systems required for these complex operations. This hiring effort highlights the company's ongoing expansion and commitment to space sustainability.

The position advertised by Clearspace is for an Applied Researcher with a specialization in Machine Learning. This role is integral to the company's mission of removing debris from Earth's orbit. The primary objective is to apply advanced machine learning techniques to real-world space operations.

Key responsibilities likely include:

• Developing algorithms for computer vision to identify and track space debris.
• Designing autonomous navigation systems for capture vehicles.
• Optimizing machine learning models for in-flight processing constraints.

The researcher will work on the cutting edge of aerospace engineering and artificial intelligence. Their work will directly impact the company's ability to safely approach and capture non-cooperative objects in space. This requires a deep understanding of both theoretical ML and practical implementation.

Clearspace is a startup operating under the Y Combinator (YC W23) accelerator program. The company is dedicated to solving the critical problem of space debris. Orbital debris poses a significant threat to active satellites and the International Space Station.

The company's approach involves active debris removal (ADR). This process includes:

1. Locating debris using advanced tracking systems.
2. Docking with or capturing the debris using specialized robotic arms or nets.
3. Deorbiting the combined mass so both the capture vehicle and debris burn up in the atmosphere.

By securing funding and support through Y Combinator, Clearspace is positioning itself as a key player in the emerging space economy. Their technology is designed to ensure the long-term sustainability of spaceflight.

While the specific job description text was not provided in the source, the title Applied Researcher (ML) implies a high level of technical proficiency is required. Applicants will likely need experience in specific domains of artificial intelligence.

Expected areas of expertise include:

• Deep Learning: Proficiency with frameworks like PyTorch or TensorFlow.
• Reinforcement Learning: Useful for training robotic control policies.
• Computer Vision: Essential for object detection and pose estimation in a space environment.

Furthermore, candidates may need a background in aerospace engineering or physics to understand the constraints of the space environment. The intersection of machine learning and aerospace engineering is the core focus of this role.

The hiring of an Applied Researcher by Clearspace reflects broader trends in the aerospace industry. There is a growing recognition that space debris is a crisis that requires immediate attention. With thousands of satellites being launched annually, the risk of collision increases.

Companies like Clearspace are leveraging the power of Machine Learning to automate processes that were previously manual or heavily reliant on ground control. The latency of communication between Earth and orbit makes autonomous decision-making essential for debris removal missions. This role is at the heart of that technological shift.

The involvement of Y Combinator also signals that investors see significant economic potential in space infrastructure services. As the Low Earth Orbit (LEO) environment becomes more crowded, services provided by companies like Clearspace will become mandatory for safe operations.