Quartermaster

Frontend Engineer

• Design, develop, and implement responsive and visually stunning user interfaces for the web portal and mobile application.
• Collaborate with product managers, designers, and back-end engineers to deliver intuitive and seamless user experiences.
• Optimize front-end code for performance, scalability, accessibility and security.
• Ensure consistent design across platforms by implementing reusable components and adhering to design systems.
• Conduct usability testing and gather user feedback to iterate and improve the UI/UX.
• Stay up-to-date with the latest UI/UX trends, techniques, and technologies.
• Debug and troubleshoot front-end issues to ensure high-quality user experiences.
• Document design and implementation processes to maintain a clear knowledge base.

Front-End Software Engineer

Technical Program Manager

• Own end-to-end planning and execution of major product development programs, from concept through production and deployment.
• Define program schedules, milestones, and critical paths across hardware, software, and manufacturing tracks.
• Facilitate cross-functional collaboration by running standups, sprint planning, and milestone reviews with engineering and ops teams.
• Identify dependencies, resource needs, and execution risks—proactively escalating and driving resolution.
• Maintain clear, up-to-date program documentation including Gantt charts, issue trackers, and progress dashboards.
• Support new product introduction (NPI) and field rollout planning, ensuring readiness across supply chain, QA, and logistics.
• Act as primary point of contact for internal stakeholders on program status, scope, and decision timelines.
• Coordinate change management, ensuring that scope or requirement shifts are clearly communicated and integrated across teams.
• Contribute to continuous improvement of program processes, tooling, and collaboration practices.
• Help ensure programs align with company OKRs and strategic priorities.

Machine Learning Engineer

• Design, train, and evaluate models for tasks ranging from object detection and classification to anomaly detection and sensor-based inference.
• Optimize model architectures and inference pipelines for performance on embedded/edge hardware under compute and bandwidth constraints.
• Contribute to dataset development and labeling strategy, including data augmentation, synthetic data generation, and domain adaptation.
• Support prototyping and experimentation across a variety of AI subfields, including computer vision, signal processing, and multi-modal fusion.
• Implement real-time pipelines for processing sensor data on-device and in cloud environments.
• Develop tools and scripts for benchmarking, data visualization, and debugging ML model performance.
• Stay current with the latest research and tools in machine learning and evaluate their applicability to our product roadmap.
• Participate in code reviews, team knowledge sharing, and internal technical documentation.
• Must be eligible to obtain/maintain a security clearance.

Applied ML Engineer

Systems Engineer

• Own end-to-end system architecture: From antenna, to analog components, to integration with software defined radio (SDR) and edge computing hardware, to data transport, to the cloud, and all the interfaces between.
• Develop and maintain system design documentation: System requirements specifications, ICDs, architecture descriptions, and future concepts.
• Resolve cross-functional technical conflicts, ensuring RF, embedded systems, compute/algorithm, and cloud development efforts are aligned to key design and performance objectives.
• Set and refine system technical requirements by evaluating tradeoffs between COTS, modified COTS, and custom developments.
• Ensure timing accuracy and synchronization across distributed systems.
• Understand best practices in characterizing and managing system-level time and frequency biases.
• Manage requirements traceability from customer mission needs through verification and validation.
• Lead technical risk identification and mitigation at the system level.
• Oversee and define the test and evaluation architecture; ensure lab test infrastructure aligns to greatest system risks to maximize durability and lifespan.

Principal Systems Engineer – RF Systems

Product Manager

• Define web product strategy and roadmap for the web application and data products, ensuring alignment with customer mission requirements, security protocols, and company objectives.
• Implement strategies to measure and actively increase Daily Active Users (DAU) and overall product engagement within the target customer base. Analyze usage data to inform feature prioritization.
• Develop an expert-level understanding of end-user workflows (e.g., intelligence gathering, mission planning, surveillance) to identify critical pain points and opportunities for platform integration and optimization.
• Drive concept development for features and products that become mission-critical and indispensable tools for our defense and government users.
• Author clear, detailed product requirements (PRDs), user stories, and acceptance criteria.
• Collaborate closely with growth, UI/UX, and technical teams to ensure customer and engineering priorities are always aligned.

Mechanical Engineer

• Design and release hardware from established requirements, including brackets, mounting interfaces, enclosures, and installation/routing solutions with attention to serviceability, sealing, vibration, and corrosion.
• Collaborate with field operations to support Smartmast deployments and troubleshoot mechanical integration challenges.
• Support cabling integration (Ethernet/PoE, coax/RF, power) by researching and sourcing components; define and validate routing/strain relief/termination standards and perform basic verification testing.
• Triage and resolve integration issues across engineering, manufacturing, and field operations by gathering evidence, identifying root cause, implementing fixes, and closing the loop with design/BOM/documentation updates.
• Support rapid prototyping R&D involving sensor integration and lightweight software design.
• Must be eligible to obtain/maintain a security clearance.

Engineer

• Lead the design, training, and deployment of computer vision and perception models for tasks such as vessel detection, classification, and tracking.
• Architect and optimize inference pipelines for real-time performance on edge devices, including GPU-accelerated and mixed-precision inference.
• Implement and refine algorithms for scene understanding, object segmentation, and environmental awareness using both image and sensor fusion data.
• Collaborate with hardware and software teams to ensure model compatibility with embedded systems and available compute resources.
• Develop techniques for edge-case detection, dataset curation, and self-healing model behavior in dynamic, non-ideal environments.
• Apply active learning, domain adaptation, and synthetic data strategies to improve model robustness and generalization.
• Work closely with RF and sensor teams to integrate complementary signal data into multimodal classification and detection pipelines.
• Own model evaluation processes and benchmarking across hardware platforms and field conditions.
• Maintain best practices for model versioning, traceability, and validation in safety-critical applications.

Machine Perception Engineer – Vision

• Research, design, and implement advanced machine learning models that combine vision, RF, and acoustic signals for detection, classification, and tracking tasks.
• Architect sensor fusion pipelines that support robust, redundant, and context-aware perception in dynamic environments.
• Collaborate closely with domain experts and systems engineers to translate raw sensor data into actionable model inputs.
• Design and oversee data pipelines for multi-modal learning, including data alignment, augmentation, and pre-processing across modalities.
• Optimize models and inference workflows for low-latency execution on embedded and edge compute platforms.
• Lead performance analysis across individual and fused modalities, and drive strategies for improving robustness and generalization.
• Prototype and operationalize novel research in sensor fusion, uncertainty modeling, and representation learning.
• Contribute to long-term architectural decisions around multi-modal AI infrastructure, tooling, and evaluation frameworks.
• Document model design, training methodology, and validation processes with rigor and clarity.
• Must be eligible to obtain/maintain a security clearance.

Machine Learning Engineer, Digital Signal Processing

Full-stack Engineer

• Remote Provisioning: Remotely configure edge nodes, activate Starlink connections, and calibrate cameras and SDRs immediately after physical installation.
• Remote Troubleshooting: Act as the escalation point for edge node health. You will independently debug Linux systems, container crashes, and hardware-software integration issues from thousands of miles away.
• Cross-Functional Collaboration: Serve as the critical bridge between the field technicians bolting the gear to the ship, the backend engineers ingesting the data, and the AI team deploying edge models.

Field Software Engineer

• Establish NIST-traceable (or equivalent) calibration capability for Matsu SDR systems. Define procedures, select equipment, and set up an in-house lab or manage contract labs end to end.
• Design, build, and maintain RF test infrastructure at our Northern Virginia office and future remote/vessel deployment sites. Own the equipment inventory, maintenance cycles, operational readiness, and coordination with hardware and software teams.
• Participate in hardware design reviews. Define testability requirements, component tolerance specs for our radio systems, peripheral hardware (e.g. GPSDOs), and custom assemblies.
• Develop and own Acceptance Test Procedures (ATPs) for execution by technicians for medium to high volume production runs. Automate where practical; define pass/fail criteria that catch previous and potential failure modes.
• Measure and validate timing accuracy, latency, and phase coherence across the full system. Document performance margins against system requirements.
• Lead calibration and characterization of system components, including GNSS hardware, antenna patterns, and more. Define the measurements, execute or delegate, and own the traceability records.
• Define and execute (or outsource) environmental qualification: maritime vibration, temperature/humidity cycling, salt spray. Coordinate with hardware engineers to know the applicable standards and when to apply them.
• Establish incoming inspection criteria for RF components. Own the qualification process for primary vendors and other supply chain partners. Flag reliability risks before they reach production.
• Leverage your network in RF test, calibration, and defense hardware to identify what fails at scale. Bring that knowledge into design and procurement decisions early.
• Maintain calibration records, test data packages, and qualification documentation to support internal quality processes and customer/program requirements.