Engineering Manager, Perception AI

YOUR MISSION

Build and lead the perception AI team that enables autonomous spacecraft operations in complex operational environments. Your team will develop world models and generative AI for photorealistic synthetic training data, multi-target tracking and sensor fusion for object discrimination, edge-deployed AI models for on-orbit operations, and camera calibration systems maintaining performance through environmental variations. You will grow the team across five work streams: world models and generative AI for synthetic video generation, multi-target tracking with clutter and decoy discrimination, signal processing and multi-sensor fusion, camera control with online auto-calibration, and edge-deployed object detection. You will define validation standards bridging synthetic data, simulation, hardware-in-the-loop testing, and operational acceptance. Your team will deliver perception AI systems that track satellites through occlusions and complex maneuvers, discriminate objects from decoys against Earth background clutter, and maintain custody for months without ground intervention. This is perception AI engineering where algorithms operate under spacecraft compute and power budgets, mistakes have operational consequences, and systems enable critical space operations.

• Provide technical direction on world model architectures for photorealistic synthetic video generation, perception AI systems combining classical computer vision with modern deep learning, and edge deployment strategies
• Review and approve technical designs for generative AI systems, multi-target tracking algorithms, sensor fusion pipelines, and model compression strategies
• Lead by example, setting the standard for technical work across design, implementation, and verification (approximately 50% of time)
• Define perception AI standards, validation approaches bridging synthetic and real-world data, and hardware-in-the-loop testing protocols
• Drive technical decisions on when to use physics-based simulation versus generative AI for synthetic data, when classical algorithms excel versus learned representations, and real-time performance trade-offs
• Maintain technical credibility in generative AI for synthetic data, computer vision, state estimation, and resource-constrained deployment

• Define and own the team roadmap in collaboration with perception AI leadership, balancing near-term delivery with long-term capability development across world models, multi-modal fusion, and continuous learning from operational and synthetic data
• Translate organizational strategy into actionable team objectives aligned to defense and commercial customer missions
• Prioritize work across competing demands: photorealistic synthetic video generation, multi-target tracking with decoy discrimination, signal processing and sensor fusion, camera calibration and control, edge-deployed object detection
• Manage dependencies and coordinate with flight software, hardware, autonomy, and simulation teams for successful cross-functional delivery
• Track and communicate progress, risks, and blockers to leadership with focus on integration milestones and operational acceptance
• Drive continuous improvement in processes, tools, and workflows to increase velocity from prototype to flight-ready systems
• Work with program managers to scope projects, estimate timelines, and commit to deliverables

• Build, grow, and lead a team of perception and AI engineers through recruiting, hiring, onboarding, and retention
• Provide direct people management including 1-on-1s, performance reviews, goal setting, and career development
• Foster a culture of technical excellence, rigorous validation, and rapid iteration from synthetic data through simulation to flight
• Develop individual contributors into technical leaders through mentorship and growth opportunities
• Manage team dynamics, resolve conflicts, and ensure healthy team communication
• Facilitate knowledge sharing, cross-training, and documentation to build team resilience
• Bridge generative AI, classical perception, and modern deep learning approaches, guiding the team on when each paradigm applies

• - Education: Master's degree in computer science, robotics, electrical engineering, aerospace engineering, or related technical field; Ph.D. or Bachelor's degree with equivalent professional experience also accepted
• Experience: 8+ years of experience in computer vision, robotics, autonomous systems, or perception AI engineering with at least 4-5 years in people management, leading teams of 4+ engineers
• People Management: Demonstrated track record of building, growing, and developing high-performing engineering teams including hiring, performance management, and career development
• Leadership: Proven ability to lead through influence, manage stakeholder expectations, drive execution in ambiguous environments, and deliver results through others
• Technical Foundation: Strong foundation in perception AI with depth in synthetic data generation, multi-object tracking, sensor fusion, or edge AI deployment (sufficient for technical oversight and credibility)
• Domain Knowledge: Experience with resource-constrained perception AI deployment, real-time vision systems, or aerospace applications operating under strict compute, power, and thermal budgets
• Communication: Exceptional communication skills with ability to influence senior leadership, translate between technical and non-technical audiences, and represent the team effectively
• Strategic Thinking: Translate organizational objectives into team strategy, prioritize competing demands, and balance technical excellence with delivery
• Process & Execution: Track record defining processes, establishing team norms, and driving execution excellence in fast-paced environments
• Collaboration: Strong cross-functional collaboration with ability to build relationships, resolve conflicts, and drive consensus across autonomy, flight software, hardware, and simulation teams
• Clearance: U.S. Citizen and eligible for government security clearance

• Prior work with synthetic data generation: generative AI for photorealistic video synthesis, rendering pipelines (Blender, Unreal), domain randomization, sim-to-real transfer
• Experience with world models and physics-based simulation: sensor phenomenology frameworks, multi-modal data synthesis
• Experience with deep learning for perception AI: object detection (YOLO, Faster R-CNN), classification, semantic segmentation, vision transformers
• Deep experience with multi-object tracking: data association algorithms (Hungarian, JPDA, MHT), Kalman filtering (EKF, UKF, IMM), track management logic
• Background in sensor fusion: multi-modal perception AI (optical, thermal, LIDAR), time synchronization, covariance intersection, information-theoretic fusion
• Experience with edge AI deployment: model compression (quantization, pruning, distillation), ONNX/TensorRT optimization, inference on embedded systems
• Background in classical computer vision: camera calibration, coordinate transformations, structure from motion, pose estimation
• Background in signal processing: sensor phenomenology, radiometry, SNR analysis, adaptive filtering, multi-spectral processing
• Experience with flight software integration: safety-critical systems, deterministic execution, hardware-in-the-loop validation
• Prior work with defense or space missions: object discrimination, space situational awareness, rendezvous and proximity operations
• Background in autonomous aerospace systems: spacecraft autonomy, UAVs, robotic systems, or defense applications
• Experience managing geographically distributed or hybrid teams through full product development lifecycles
• Track record delivering complex, multi-quarter initiatives requiring coordination across multiple teams and hardware/software integration
• Experience with agile methodologies, sprint planning, and iterative delivery in engineering environments
• Background partnering with product management to define requirements and prioritize features based on technical leverage
• Experience with resource planning, capacity modeling, and headcount justification
• Prior work bridging research prototypes (CVPR/ICCV/ICRA-quality) to production systems with emphasis on reliability and operational deployment

• Work Location—Successful candidates will be located near Denver, CO or Long Beach, CA. 
• Work environment—the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
• Physical demands—the physical demands of the job, including bending, sitting, lifting and driving.

This position will be open until it is successfully filled. To submit your application, please follow the directions below.

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