Senior RF Machine Learning Engineer

Senior RF Machine Learning Engineer

Our Processing Team builds advanced DSP, geolocation, and analytics capabilities spanning embedded platforms and large-scale cloud infrastructure. We are seeking a senior engineer who combines theoretical grounding in signal processing and machine learning with experience applying these techniques to large, real-world datasets in production environments. In this role, you will develop and mature RF signal processing and machine learning techniques, work end-to-end across the processing stack, and collaborate closely with partner teams to deploy robust, scalable solutions into production.

As the Senior RF Machine Learning Engineer, your main responsibilities will be:

• Design, implement, and improve RF signal detection, characterization, and geolocation algorithms across a wide range of signals and operational scenarios.
• Identify opportunities where machine learning can augment or complement traditional signal processing approaches in the current processing stack.
• Work with our customer solutions team to understand signal processing and analytic techniques that drive novel customer insights and product capabilities.
• Work with large volumes of raw RF and derived data to build scalable, reliable processing and analytics pipelines.
• Contribute technical leadership through design reviews and mentorship of other engineers.

Your skills and qualifications: Essential education and experience:

• B.S. or higher in Electrical Engineering, Computer Engineering, Computer Science, Applied Physics, or a related technical discipline or equivalent experience
• 2+ years of hands-on experience applying signal processing and machine learning techniques to real-world data in a production environment.
• Strong foundation in signal processing concepts (e.g., signals and systems, detection theory, estimation, filtering).
• Experience applying classical machine learning and statistical techniques such as clustering, classification and anomaly detection to complex datasets.
• Fluency in Python, common Python scientific computing libraries (e.g., NumPy, pandas, PyTorch) and experience working within modern Python-based technical stacks.
• Experience manipulating and analyzing large, raw datasets in support of signal processing and analytics workflows.
• Strong communication skills and a collaborative, team-oriented mindset.

Desirable:

• M.S. or Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• Direct experience applying signal processing and/or machine learning techniques to RF data.
• Familiarity with modern machine learning approaches such as deep learning.
• Experience optimizing processing performance through parallel computing.
• Experience deploying software in containerized or cloud-native environments (Docker, Kubernetes).
• C++ experience.

Base Salary Range: $140,000 - $170,000 annually HawkEye 360 offers a compensation package that includes a competitive base salary plus annual performance bonus and benefits. We consider many factors when determining salary offers, such as candidate's work experience, education, training & skills, as well as market and business considerations. We are also open to considering candidates with experience and qualifications at a different level than required in a job posting, which may affect the compensation package offered.

Company Overview: HawkEye 360 is delivering a revolutionary source of global knowledge based on radio frequency (RF) geospatial analytics to those working to make the world a safer place. The company operates a commercial satellite constellation that detects, geolocates, and identifies a broad range of signals & behaviors. We employ cutting edge AI techniques to equip our global customers with high-impact insights needed to make decisions with confidence. HawkEye 360 is headquartered in Herndon, Virginia.

HawkEye 360 is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity Employer, making decisions without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, marital status, national origin, age, veteran status, disability, or any other protected class.