Machine Learning Engineer, Radar Perception
BLUE ORIGIN
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Machine Learning Engineer, Radar Perception
BLUE ORIGINCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying deep learning models, particularly in radar data processing and characterization. Proficient in Python and experienced with edge deployment technologies, ensuring robust model performance in real-world applications.
Highest-signal resume keywords
Deep Learning Model DevelopmentEdge Deployment ExperienceRadar Fundamentals KnowledgePython Programming SkillsUncertainty Quantification
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Design, train, and evaluate deep learning models, including GNNs, transformers, DRL, Learned Algorithm Unfolding, or hybrids
- Find and classify objects of interest in raw radar data, range-Doppler maps, and range-angle maps
- Build characterization models estimating object size, shape, motion state, track, behavior, and calibrated uncertainty
- Represent radar data appropriately for each model, including point or track graphs for GNNs
- Build the data engine through synthetic data generation, simulation-based training, labeling strategy, augmentation, and class-imbalance handling
- Detect inputs outside the training data and flag low-confidence results
- Work with Advanced Systems, DSP, GNC, and flight software teams to define data handoffs and deploy models on embedded or edge hardware
- Define metrics, test sets, and evidence needed to qualify ML models for edge deployment
- Own the AI layer that processes raw radar data and characterizes objects for autonomy systems or operators
- Define radar AI architecture for a new development program
Requirements
What you’ll need- BS in Computer Science, Electrical Engineering, Computer Engineering, Physics, or a related field
- About 3 years of ML engineering experience
- Track record of building and shipping deep learning models in PyTorch or similar
- Edge deployment experience with TensorRT, quantization, FPGA, or SoC targets
- Strong Python skills
- Working knowledge of radar fundamentals: range, Doppler, angle, resolution, clutter, and multipath
- Familiarity with GMTI or AMTI signal processing concepts and implications on system design
- Experience with sensor data from radar, sonar, SAR, lidar, or RF
- Experience training on synthetic or simulated data and closing the simulation-to-real gap
- Experience with uncertainty quantification, calibration, or out-of-distribution detection
- Experience with embedded C++
- Ability to obtain a TS/SCI clearance
- U.S. citizenship, nationality, permanent residency, refugee status, or asylum status required under export control regulations
- Ability to pass required background checks
Benefits
Comp & perks- Relocation assistance
- Medical, dental, and vision insurance
- Basic and supplemental life insurance
- Paid parental leave
- Short- and long-term disability
- 401(k) with company match of up to 5%
- Education Support Program
- Stock options for all regular employees working at least 20 hours/week
- Up to four weeks of paid time off per year based on weekly scheduled hours
- Up to 14 company-paid holidays
- Potential eligibility for benefits and bonuses based on role type, job level, individual contributions, and company discretion
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Deep LearningMachine Learning EngineeringModel EvaluationSynthetic Data GenerationUncertainty QuantificationEmbedded C++QuantizationFPGASoC TargetsSimulation-Based Training
Tools & Technologies
PyTorchTensorRTGNNsTransformersDRLLearned Algorithm UnfoldingRadar Data ProcessingSimulation ToolsLabeling StrategyAugmentation Techniques
Certifications & Qualifications
TS/SCI Clearance
Industry Keywords
RadarGMTIAMTISensor DataDopplerRangeAngleResolutionClutterMultipath