Staff Engineer, Autonomy Capabilities – Maritime (R5325)
at Shield AI
Location
Washington, D.C.; Boston, Massachusetts; San Diego, California; Dallas, Texas
Compensation
$221k–$331k USD
Type
full time
Posted
1 months ago
Market range · company + function + seniority
p25 · target · p75 · n=268
Posted $331k · well above market
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Job description
The Autonomy Capabilities Team develops functionality to automate single- and multi-agent teams of platforms in pursuit of mission objectives and acts as a central discipline-aligned focal-point for encouraging consistent usage of technical approaches across business portfolios. This team operates at all levels of the command-and-control hierarchy (from advanced control laws, through motion planning, and up to advanced tactical behaviors and multi-agent coordination) and across a multitude of mission sets, platform types, and operational domains (e.g., air, sea, space). The team puts a strong emphasis on both fundamentals (e.g., aircraft kinematics/dynamics, trajectory design, optimization, information fusion, efficient algorithms, etc.) and software integration skillsets (e.g. structures, protocols, threading, interface management, etc.) to bring market-differentiating capabilities to customers that seamlessly operate within their integration contexts.
This position is perfect for an individual who enjoys solving complex problems across a diverse set of programs and integration contexts. An ideal candidate is expected to address operational system needs through a multitude of advanced methodologies that blend traditional control system approaches with advanced optimization. Developed solutions are expected to be integrated into real-world platforms with near-term program impacts and rewards and so balancing theory with practice and rigorous implementation is paramount.
About the Job:
What you'll do:
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Robust Motion Planning – Automate unmanned surface vehicle (USV) and/or unmanned underwater vehicle (UUV) maneuvering through advanced motion planning and adherence to safety constraints including keep-out volumes, cooperative and uncooperative vehicles, and depth limitations
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Localization and Tracking – Employ cutting edge approaches for passive and active localization by defining fusion modalities, sensor requirements, and observability-aware maneuvering capabilities to maximize information gleaned from single- or multi-agent sensor networks
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Safe Maritime Operations – Keep vehicle safety as a central focus within autonomous behavior architectures to provide run-time assurance, ensure COLREGS adherence, and build operator trust in advanced mission autonomy
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Heterogeneous Multi-Agent Collaboration – Develop distributed optimization and task allocation approaches for heterogenous teaming in order to accomplish mission objectives by maximally utilizing the diverse capabilities of agents to overall team advantage
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Rigorous Software Development – Develop high-performance software modules that are well-tested, have clear interfaces and span-of-control, and are ready for mid-to-high Technology Readiness Level (TRL) insertions
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Cross-Functional Collaboration – Collaborate with cross-functional teams including perception, planning, simulation, hardware, and operational test to ensure seamless integration of autonomy solutions on real-world platforms
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Deployment & Test Support – Deploy autonomy capabilities to real surface vehicles, supporting integration and demonstration events as well as analysis of mission logs to verify software performance and validate system operational effect
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R&D and Road-mapping – Contribute to autonomy roadmaps by researching and prototyping new algorithms, identifying tactical capability gaps, and proposing novel solutions that advance Shield AI’s mission
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Travel Requirement – Members of this team typically travel around 25% of the year (to different office locations, customer sites, and flight integration events).
Required Qualifications:
Preferred Qualifications:
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Experience in multiple aerospace domains (e.g., air, land, sea, space)
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Experience across a breadth of methodologies from classical control through optimization and applied ML/RL
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Background in collaborative behaviors and swarm robotics
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Familiarity with domain-relevant DoD or government programs
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Hands-on experience supporting integration events, customer demos, and/or live exercises
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