Machine Learning Engineer

Rebel Space Technologies provided pay range

This range is provided by Rebel Space Technologies. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$/yr - $/yr

Rebel Space Technologies is seeking a talented and experienced Machine Learning Engineer to join our team.

Machine Learning Engineer:

At Rebel Space, our mission is to protect critical space infrastructure through enhanced observability and space system cybersecurity. We believe that as space infrastructure expands, it will be increasingly difficult to secure and monitor these systems against critical failures or evolving cyber threats. To address this, we are building software that empowers developers and operators to rigorously evaluate and secure their systems from conception through to operations.

Our technology supercharges space infrastructure, ensuring resilience against evolving threats in an increasingly complex environment. We’re looking for a talented Machine Learning Engineer to join us in pioneering the next generation of space system security.

As a Machine Learning Engineer at Rebel Space, you’ll design, implement, and optimize ML models for anomaly detection in satellite communications. You’ll help architect secure, scalable infrastructure for ML workloads within demanding government and defense compliance environments. This position is ideal for someone who thrives in dynamic, fast-paced R&D environments, is comfortable building systems from the ground up, and has a bias toward elegant, performant, and reliable code.

Responsibilities:

• Develop and maintain machine learning infrastructure that is portable and flexible, supporting deployments both in cloud environments and on edge devices
• Research, prototype, and survey different ML architecture and workflow optimization techniques
• Design and implement proof-of-concept custom optimizations, then demonstrate how these optimizations improve the performance of existing machine learning models when applied to actual real-world datasets.
• Build data collections, labeling pipelines, and evaluation pipelines. Research and develop machine learning models for physical sensor systems.
• Extend existing ML libraries and frameworks.
• Create and deliver reliable software through requirements generation, continuous integration, automated testing, issue tracking, and code reviews.
• Own technical projects from start to finish and be responsible for major technical decisions and tradeoffs. Effectively participate in team planning, code reviews, and design discussions.

Basic Qualifications:

• Bachelor's degree in Computer Science, Electrical Engineer, Physics or related technical discipline.
• 3+ years of relevant industry experience in data analytics and machine learning
• Strong expertise in scientific Python (Num Py, Sci Py, Pandas) and modern ML frameworks (PyTorch, Tensor Flow, JAX, Scikit-Learn, Keras)
• Proficiency in SQL and experience with relational or time-series databases
• Proven experience applying statistical modeling, data analysis, and inference to extract actionable insights from large and complex datasets
• Familiarity with overall big data analysis, system backend integration with new ML systems, and large-scale data processing
• Proficiency in data visualization tools (e.g., Matplotlib, Seaborn, Power BI) to effectively communicate findings
• Excellent understanding of algorithms, data structures, and coding standards
• Strong communication and behavioral skills
• Motivated self-starter that can work autonomously and as part of a team

Preferred Qualifications:

• PhD, Masters, or equivalent in Computer Science, Electrical Engineer, Physics or related field with 5+ years of professional experience in machine learning engineering
• Knowledge of experiment tracking, model deployment strategies, data versioning, and monitoring
• Experience with ML infrastructure tools (e.g. MLflow, Kubeflow, Airflow, feature stores, model registries)
• Prior experience with real-time data processing, prediction systems, or active learning pipelines
• Exposure to synthetic data generation techniques (GANs, simulation platforms).
• Experience designing reliable software through requirements…