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Engineering Manager (Autonomy)

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Onsite

Engineering Manager (Autonomy)

True Anomaly

Engineering Manager (Autonomy)

Skills

Deep LearningProduct ManagementMachine LearningAI

2 months ago

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Important information


Contract type:

Permanent

Salary:

$205,000 – $315,000 / year

Location:

Denver, CO, USA

Work mode:

Onsite

Published on:

2 September 2026

What they need


  • As Engineering Manager, Autonomy, you will build and lead a team of autonomy engineers who develop multi-agent resource allocation systems and edge-deployed physical AI capabilities for spacecraft operations. You will be responsible for team growth, performance, and delivery while ensuring alignment with the broader autonomy roadmap spanning constellation scheduling, onboard replanning, and distributed coordination
  • You will translate technical vision into execution, develop talent, and create the processes and culture that enable your team to deliver validated, rugged autonomy systems that operate under compute and latency constraints and evolve through continuous learning. Your primary focus is people leadership, with technical contributions focused on strategic direction, architecture reviews, and maintaining credibility with your team
  • Technical Oversight & Strategic Contributions
  • Provide technical direction on multi-agent coordination architectures, edge deployment strategies, and validation approaches for constrained optimization problems in spacecraft operations
  • Review and approve technical designs for combinatorial optimization algorithms, stochastic planning systems, and distributed coordination protocols
  • Participate in all aspects of a project’s development lifecycle, including design, implementation, and verification. Lead the team by example, setting the standard for technical work (approximately 35% of time)
  • Define autonomy standards, explainability requirements, and validation approaches that enable deployment in high-stakes environments
  • Drive technical decisions on compute allocation, edge optimization strategies, and real-time performance trade-offs
  • Maintain technical credibility in operations research, reinforcement learning, and edge AI deployment
  • Strategic Planning & Execution
  • Define and own the team roadmap in collaboration with autonomy leadership, balancing near-term delivery (flight demonstrations, customer test campaigns) with long-term capability development (continuous learning, end-to-end neural planning)
  • Translate organizational strategy and technical vision into actionable team objectives aligned to customer missions
  • Prioritize work across competing demands: scheduling system maturation, edge autonomy for proximity operations, multi-spacecraft coordination, anomaly detection agents
  • Manage dependencies and coordinate with hardware, trajectory planning, and flight software teams to ensure successful cross-functional delivery
  • Track and communicate team progress, risks, and blockers to leadership and stakeholders with focus on demonstration readiness and customer deliverables
  • Drive continuous improvement in team processes, tools, and workflows to increase velocity from prototype to production-ready systems
  • Work with technical program managers to scope projects, estimate timelines, and commit to deliverables
  • Team Leadership & People Management
  • Build, grow, and lead a team of autonomy 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 evaluation, and rapid iteration from concept to flight
  • Develop individual contributors into technical leaders through mentorship, coaching, and growth opportunities
  • Manage team dynamics, resolve conflicts, and ensure healthy team communication and collaboration
  • Facilitate knowledge sharing, cross-training, and documentation practices to build team resilience and institutional knowledge
  • Bridge air domain autonomy expertise with space domain constraints (orbital mechanics, communication latency, compute/power budgets)

Benefits

  • 100% Employer-Sponsored Healthcare: We’ve got you covered! Our comprehensive healthcare package is fully sponsored, ensuring you and your loved ones stay healthy and worry-free, so you can focus on shaping the future of space security.
  • Generous Time Off: We believe in working hard and recharging fully. Our generous vacation policy gives you the flexibility to take the time you need to relax, explore, and come back energized to take on new challenges.
  • Equity: Own an equity stake in True Anomaly as you build something transformative. With equity options, you’re not just working for True Anomaly; you’re an owner. As an essential part of our growth and future success, we want you to share in the upside potential!
  • 401k and Roth 401k options: Plan for tomorrow with our 401K and Roth 401K options that put you in control of your financial future.
  • Learning & Development Opportunities: Your growth fuels our innovation. We’re committed to providing you with the resources and training to expand your skills, spark new ideas, and advance your career with us.- People Management:Demonstrated track record of building, growing, and developing high-performing engineering teams including hiring, performance management, and career development
  • Strategic Thinking:Ability to translate organizational objectives into team strategy, prioritize competing demands, and make trade-offs that balance technical excellence with delivery
  • Technical Foundation:Strong foundation in autonomous systems with depth in areas such as multi-agent coordination, combinatorial optimization, reinforcement learning, or constrained planning (sufficient to provide technical oversight and credibility)
  • Education:Master’s degree in computer science, aerospace engineering, robotics, applied mathematics, operations research, or related technical field; Ph.D. or Bachelor’s degree with equivalent professional experience also accepted
  • Clearance:U.S. Citizen and eligible for government security clearance
  • Leadership:Proven ability to lead through influence, manage stakeholder expectations, drive execution in ambiguous environments, and deliver results through others
  • Process & Execution:Track record of defining processes, establishing team norms, and driving execution excellence in fast-paced environments
  • Communication:Exceptional written and verbal communication skills with ability to influence senior leadership, translate between technical and non-technical audiences, and represent the team effectively
  • Collaboration:Strong cross-functional collaboration skills with ability to build relationships, resolve conflicts, and drive consensus across hardware, software, and trajectory planning teams
  • Domain Knowledge:Experience with real-time planning systems, resource-constrained deployment, or aerospace applications where autonomy operates under strict compute, latency, and power constraints
  • Experience:8+ years of experience in autonomous systems, robotics, AI/ML, or aerospace engineering with at least 4 years in people management, leading teams of 4+ engineers
  • Experience managing geographically distributed or hybrid teams
  • Experience managing teams through full product development lifecycles from early prototypes through operational deployment
  • 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 in autonomous aerospace systems: UAVs, spacecraft autonomy, robotic systems, or similar applications
  • Background in multi-agent reinforcement learning: MARL, policy learning, reward shaping, credit assignment, or cooperative game theory
  • Deep experience with operations research: mixed-integer programming, constraint satisfaction, graph optimization, auction algorithms, or task allocation
  • Experience with edge AI deployment: model compression, quantization, inference optimization, embedded ML systems, or real-time neural network execution
  • Background in stochastic optimization, robust planning under uncertainty, or Monte Carlo methods for decision-making
  • Experience with trajectory optimization, motion planning, or sequential decision-making in constrained environments
  • Background partnering with product management to define requirements and prioritize features based on technical leverage
  • Prior work with hierarchical planning systems, temporal reasoning, or hybrid discrete-continuous optimization
  • Experience with resource planning, capacity modeling, and headcount justification
  • Prior work bridging research prototypes (ICAPS/NeurIPS-quality) to production systems with emphasis on scalability and reliability

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