Shield AI

Recruitment

• Own Shield AI's university recruiting strategy end to end, including target school portfolio, annual plan, calendar, and budget
• Lead demand collection for interns, co-ops, and intern-to-FTE conversions, driving approvals and requisition postings on schedule
• Build Shield AI's campus brand through technical storytelling, faculty and career center relationships, and engagement with robotics, AI, and diversity student organizations
• Represent Shield AI at career fairs, tech talks, and information sessions, and build early candidate pipelines
• Run full-cycle recruiting for intern, co-op, and new graduate roles across software, systems, electrical, integration and test, and mechanical engineering
• Maintain a structured, bias-reduced evaluation process and conduct intern interview training and calibration
• Extend offers within the standard framework and partner with Compensation on salary and housing stipend benchmarking
• Own the accepted-offer-to-Day-1 experience and strengthen onboarding readiness
• Partner with the intern program team on mentorship matching, project scoping, reviews, and intern events
• Own the intern conversion cycle through return-intern and full-time offers
• Report on demand fulfillment, offer and acceptance rates, conversion, diversity, and school ROI to reshape recruiting strategy
• Present school portfolio and ROI analysis to senior leaders, run technical calibration with hiring managers, and close competitive candidates

Recruiter, University and Alternative Channel

People Operations

• Define and execute a unified People Operations vision, operating model, and multi-year roadmap spanning candidate interviewing, hiring, onboarding, and employee lifecycle operations
• Lead day-to-day operations and ensure consistent, high-quality service delivery across the employee lifecycle
• Lead and scale the People Operations function, with accountability for team performance, service quality, and outcomes
• Establish team structure, capacity planning, KPIs, and operating rhythms
• Advise executives on workforce operations, hiring experience, service delivery, and AI enablement
• Transform People Operations into a self-service-first, AI-enabled support model
• Design and implement automation, case management, and digital support channels
• Lead enterprise knowledge management
• Own service performance, including SLAs, response times, quality, and employee satisfaction
• Establish governance for AI-enabled execution and support
• Improve services through analytics, feedback, and process optimization
• Select and manage People Operations vendors and platforms
• Own end-to-end workforce lifecycle processes from interview coordination and candidate logistics through onboarding, employee changes, and offboarding
• Lead process redesign, automation, change management, and adoption initiatives
• Establish policies, documentation, controls, service standards, operational continuity, and audit readiness
• Ensure employee data accuracy, security, and integrity
• Establish data governance, audit, and validation practices
• Partner with HRIS, Payroll, Legal, Finance, IT, Talent Acquisition, and Digital teams
• Execute the People Operations technology roadmap and optimize UKG, Jira Service Management, Ashby, and automation tools
• Establish service metrics, dashboards, and reporting frameworks
• Monitor SLAs, KPIs, and operational health indicators
• Use data to identify trends, inefficiencies, and automation opportunities
• Measure service effectiveness, employee experience, and AI performance
• Evaluate emerging technologies, industry trends, and best practices to modernize and scale People Operations

Director, People Operations

Financial Planning and Analysis

• Serve as the lead finance partner supporting a strategic program throughout its lifecycle
• Partner cross-functionally with Program Management, Engineering, Contracts, and other stakeholders to support financial execution and business decision-making
• Lead program forecasting, Estimate at Completion (EAC), financial planning, and variance analysis while identifying risks and opportunities
• Develop financial models, dashboards, and reporting tools that improve visibility into program performance and support data-driven decisions
• Drive continuous improvement initiatives across the Hivemind Program Finance organization by standardizing processes, improving reporting, and implementing scalable financial tools
• Introduce and implement industry best practices to improve program financial management and organizational effectiveness
• Mentor junior Program Finance team members by providing technical guidance, coaching, and support for their professional development
• Partner with finance leadership on strategic initiatives, process transformations, and special projects that strengthen the Program Finance function
• Prepare executive-level reporting and communicate financial performance, risks, and recommendations to program and business leadership
• Support program startup activities, contract execution, and financial planning for new program efforts as needed

Lead Program Finance Analyst

Full-stack Engineer

• Research, design, and implement state-of-the-art algorithms for optimal task allocation, scheduling and temporal sequencing of heterogeneous teams of autonomous vehicles (land, air, other).
• Solidify and improve existing C++ based mission planning software applicable across disparate vehicle types and compute platforms.
• Work with our engineers, program managers, and product managers to define a technical roadmap for future autonomy solutions and SDK offerings.

Senior Staff Engineer, Discrete Planning

Analytics Engineer

• Own Gold-layer design and delivery, including facts, dimensions, domain data marts, and governed KPI implementations on Databricks.
• Build and maintain the enterprise semantic layer through curated views, governed semantic models, metric definitions, and reusable patterns for trusted business consumption.
• Translate approved KPI and metric definitions into precise, testable, auditable transformation logic that matches agreed business meaning.
• Define and enforce the promotion path from domain Gold to enterprise Gold, ensuring shared metrics are not published without required business and governance sign-off.
• Partner directly with business stakeholders across domains to clarify KPI definitions, challenge ambiguity, and resolve competing definitions before implementation.
• Enable domain teams by creating modeling standards, reusable design patterns, review processes, and coaching mechanisms rather than acting as the long-term owner of every downstream use case.
• Review Gold-layer models created by other teams or partners for correctness, definition integrity, usability, and conformance to enterprise standards.
• Optimize Gold-layer structures for BI and self-service analytics consumption while preserving traceability, governance, and metric consistency.
• Ensure semantic models, tables, columns, ownership, and business definitions are documented and discoverable.
• Apply awareness of data sensitivity, classification, and approved use when designing joins, dimensions, semantic views, and access patterns so the semantic layer reflects both business meaning and compliance requirements.

Staff Analytics Engineer, Databricks

Data Engineer

• The Senior Staff Data Engineer will help build and operate the enterprise lakehouse on Databricks, creating the governed data foundation that supports multiple business domains and downstream analytics.
• Responsible for scalable ingestion, reliable data processing, and strong technical controls across the Bronze and Silver layers of the medallion architecture.
• Design and build ingestion pipelines from enterprise source systems into the Databricks lakehouse using Delta Lake.
• Own Bronze-layer ingestion, including raw landing patterns, metadata capture, load traceability, and recoverable ingestion design.
• Build Silver-layer pipelines for cleansing, standardization, deduplication, conformance, and quality enforcement.
• Define and evolve reusable ingestion and transformation patterns, templates, and engineering standards.
• Implement and maintain Databricks platform constructs needed for secure delivery.
• Build and maintain CI/CD pipelines for data platform assets.
• Apply data classification, segregation, and handling requirements within the pipeline design.
• Build data quality controls that reflect actual business meaning, record integrity, completeness, and expected domain behavior.
• Maintain documentation for source objects, ingestion logic, applied transformations, data quality rules, and known limitations.
• Partner with the Analytics Engineer and domain teams to ensure Silver-layer data is reliable, well-governed, and suitable for trusted Gold-layer modeling.
• Collaborate with domain engineering teams to align on ownership boundaries, onboarding patterns, data contracts, and support expectations as new domains are enabled onto the platform.

Senior Staff Data Engineer, Databricks

Platform Engineer

• Define and evolve enterprise AI architecture patterns for LLM integration, retrieval-augmented generation, agentic workflows, prompt orchestration, and workflow automation.
• Create reference architectures, design reviews, decision records, and implementation guidance that enable consistent AI development across business units.
• Serve as a technical authority for AI platform decisions, including model selection, integration approaches, data boundary enforcement, and lifecycle management.
• Evaluate emerging AI technologies and recommend fit-for-purpose adoption paths aligned to security, operational, and enterprise architecture requirements.
• Partner with product, platform, and business technology teams to identify common needs and convert them into reusable engineering patterns.
• Design and build reusable AI components such as connectors, agents, skill templates, prompt libraries, data pipelines, integration adapters, and service APIs.
• Lead technical design for shared platform services for AI observability, logging, usage metering, evaluation, and lifecycle management.
• Establish quality, versioning, deprecation, documentation, and contribution standards for the shared AI component catalog.
• Guide teams through adoption of shared components, balancing standardization with practical implementation needs.
• Identify opportunities to eliminate duplicate AI engineering efforts through consolidation, abstractions, and platformization.
• Architect engineering controls for access management, data classification enforcement, prompt safety, output validation, audit logging, and policy adherence.
• Partner with Security, Legal, and compliance stakeholders to embed responsible AI requirements into development and deployment pipelines.
• Design model and agent lifecycle governance patterns, including version tracking, evaluation, drift monitoring, rollback, and deprecation workflows.
• Build technical dashboards and telemetry that expose adoption, risk, performance, and governance compliance across AI-enabled systems.
• Represent engineering considerations in AI governance reviews and translate policy requirements into implementable technical standards.
• Develop AI-assisted workflow patterns that improve individual productivity, team collaboration, knowledge retrieval, meeting intelligence, document generation, and task automation.
• Design measurement approaches that connect AI usage to time savings, quality improvement, error reduction, capacity creation, and business value.
• Partner with Finance and platform teams to develop cost metering, showback/chargeback, and optimization mechanisms for AI services.
• Mentor senior and mid-level engineers, raise engineering quality, and lead complex cross-functional technical initiatives from concept through production.
• Contribute to communities of practice, internal enablement material, and technical evangelism for enterprise AI engineering standards.

Staff Engineer, AI Platform – Architecture

AI Engineer

• Build AI-assisted tools, workflow automations, agents, prompts, and integrations that reduce manual effort and improve individual and team productivity.
• Partner with business stakeholders to understand high-friction workflows, translate them into technical requirements, and deliver fit-for-purpose AI solutions.
• Implement AI-augmented collaboration patterns such as meeting intelligence, document generation, contextual knowledge retrieval, task automation, and internal assistant workflows.
• Develop and maintain internal enablement assets including prompt templates, agent examples, skill templates, playbooks, and usage guidance.
• Collect user feedback and operational telemetry to improve adoption, usability, reliability, and measured impact.
• Build and maintain reusable AI components including connectors, integration adapters, prompt modules, data pipelines, skill templates, and service wrappers.
• Contribute to shared component libraries using established quality, documentation, versioning, testing, and deprecation practices.
• Integrate AI capabilities with enterprise systems, collaboration tools, knowledge repositories, data platforms, and workflow automation platforms.
• Create developer-facing documentation, examples, and onboarding material that help other teams adopt shared AI components safely and efficiently.
• Identify repeatable patterns from project work and convert them into reusable assets for broader enterprise use.
• Implement engineering controls for data handling, access management, prompt safety, output validation, audit logging, and secure integration patterns.
• Follow enterprise AI architecture and governance standards while escalating gaps, risks, or implementation challenges to technical leads.
• Build or maintain dashboards for AI usage, adoption, policy adherence, cost visibility, error patterns, and operational health.
• Support model, prompt, and agent lifecycle activities such as evaluation, version tracking, testing, rollout, monitoring, and rollback.
• Participate in security, privacy, and governance reviews by providing implementation details, evidence, and remediation support.
• Instrument AI solutions to capture usage, performance, cost, quality, and productivity metrics.
• Support cost optimization work through usage analysis, model efficiency improvements, license rationalization inputs, and service tuning.
• Help connect AI solution usage to measurable outcomes such as time savings, error reduction, throughput improvement, and capacity creation.
• Collaborate with Engineering, IT, Security, Legal, Data, Finance, and business unit teams to deliver reliable AI capabilities in a matrixed environment.
• Contribute to AI communities of practice by sharing lessons learned, reusable patterns, demos, and implementation guidance.

Senior Engineer, AI Engineering

Solutions Engineer

• Coordinate software and hardware integration activities that enable the autonomy team to deploy and test their stack across a range of platforms, including small UAS, manned fixed-wing aircraft, and unmanned fixed-wing surrogate platforms.
• Drive sensor selection for EO/IR and A-PNT perception software testing on operationally relevant or surrogate platforms; support flight test engineers in sensor bring-up, characterization, calibration, and maintenance.
• Own the fly-fix-fly cycle for autonomy and perception software: integrate the Hivemind Pilot stack onto target hardware, verify correct deployment and configuration, define test objectives for flight test campaigns, help execute tests in the field, and analyze collected data to determine pass/fail status against objectives.
• Own sensor calibration and time synchronization processes for all A-PNT and perception sensors used in testing, including camera intrinsic/extrinsic calibration and camera-IMU temporal alignment.
• Establish data quality standards and curate data management processes for field-collected sensor data, ensuring that recordings from flight test campaigns are usable for offline analysis and algorithm development.
• Resolve technical and logistical ambiguities at the boundary between software development and flight test operations, and make command decisions that keep perception maturation efforts moving forward.

Senior Staff Engineer, Autonomy Integration

Backend Engineer

• support the development of real-time perception capabilities for Shield AI's autonomous systems.
• designing, developing, and integrating advanced computer vision algorithms into high-performance C++ software pipelines.
• focus on building custom perception capabilities from the ground up, with an emphasis on real-time performance, reliability, and deployment in edge compute environments.
• increase the team's capacity and technical depth in real-time computer vision, image processing, and applied machine learning.
• translate research concepts into deployable software and work on bespoke algorithms rather than relying only on existing libraries.
• support the development of geometry-based computer vision pipelines, including applications such as camera modelling, intrinsic and extrinsic calibration, image-to-world projection, geolocation from imagery, visual registration, and multi-sensor spatial alignment.
• familiarity with photogrammetry, epipolar geometry, bundle adjustment, terrain or map-based registration, and deployment of geometry algorithms into real-time C++ systems.

Staff Engineer, Computer Vision, C++

• Support the development of real-time perception capabilities for Shield AI's autonomous systems.
• Design, develop, and integrate advanced computer vision algorithms into high-performance C++ software pipelines.
• Build custom perception capabilities from the ground up with an emphasis on real-time performance, reliability, and deployment in edge compute environments.
• Increase the team's capacity and technical depth in real-time computer vision, image processing, and applied machine learning.
• Translate research concepts into deployable software, comfortable working on bespoke algorithms rather than relying only on existing libraries.
• Develop tracking and spatial perception pipelines for applications such as visual localization, motion estimation, scene reconstruction, landmark tracking, map building, and perception-assisted navigation.

Senior Staff Engineer, Computer Vision, C++

• Support the development of real-time perception capabilities for Shield AI's autonomous systems.
• Design, develop, and integrate advanced computer vision algorithms into high-performance C++ software pipelines.
• Build custom perception capabilities from the ground up, focusing on real-time performance, reliability, and deployment in edge compute environments.
• Translate research concepts into deployable software while working on bespoke algorithms.
• Support the development and integration of learned perception models into real-time computer vision pipelines.
• Improve model performance across varied conditions and work with datasets to enhance robustness.

Senior Engineer, Computer Vision, C++

Artificial Intelligence

• Own the single demand pipeline across Workplace AI: intake, shaping, prioritization, buy/build recommendation, rollout, adoption, and value measurement.
• Partner directly with business leaders, including senior executives, to surface needs, pressure-test requests, and distinguish real opportunities from low-value noise.
• Translate ambiguous business problems into clear requirements, decision-ready use cases, and a prioritized portfolio of AI opportunities.
• Identify where AI can create the most leverage, whether through productivity, cycle-time reduction, quality improvement, risk reduction, or better decision support.
• Lead build-versus-buy decisions, including market scans, vendor evaluation, recommendation development, and ownership of purchased-solution rollout.
• Establish baselines, define success metrics, and build the ROI cases that show whether a solution is working after deployment.
• Own adoption as seriously as selection: partner with the business on training, behavior change, communications, and local champion models so tools actually stick.
• Serve as the senior business-facing counterpart to the VP, integrating business pull and technical push into one clear prioritization and operating rhythm.
• Build, lead, and develop a team responsible for demand shaping, triage, and business-facing execution, establishing standards and processes that scale as the function matures.
• Prepare decision memos, frame tradeoffs, and resolve most prioritization questions before they reach the VP.
• Act as the trusted AI advisor to non-engineering leaders, communicating in the language of outcomes, constraints, and business value.
• Own the strategic roadmap and budget for the demand-to-value function, aligning it to enterprise priorities over multi-quarter horizons.

Director of Business Value, Enterprise AI

• Evaluate, benchmark, fine-tune, and adjust configurations of ML and generative AI models (including LLMs) in production, applying working knowledge of model training, tuning, and evaluation methodologies rather than relying solely on vendor documentation.
• Apply applied AI/ML research and emerging techniques to inform build-vs-buy decisions, model selection, and architecture choices across the AI portfolio.
• Partner directly with data science and ML engineering teams on model performance issues, drift detection, and retraining or reconfiguration needs, contributing technical judgment rather than acting purely as a liaison.
• Maintain technical fluency in prompt engineering, retrieval-augmented generation, agentic/orchestration frameworks, and the practical distinctions between generative AI and traditional ML systems, and translate those distinctions into governance and staffing decisions.
• Own AI sustainment and governance for workplace AI tools across enablement, bought solutions, and custom builds, acting as the central "run" function for the AI strategy, and building the team and processes to scale it.
• Manage all AI-related licenses and entitlements: monitor usage, optimize allocations, drive reallocation, and partner with Finance for cost visibility and optimization.
• Monitor production usage patterns and performance to recommend roadmap items, enhancements, and deprecations based on real-world dynamics in production.
• Own and triage support tickets for workplace AI tools and platforms, driving resolution across vendors, internal engineering, and security partners.
• Lead continuous evaluations of AI tools and models, including monitoring drift, benchmarking performance, and ensuring tools remain effective and aligned with business KPIs.
• Maintain the updates and security outlook for AI platforms, coordinating patches, version upgrades, vulnerability remediation, and compliance with Shield AI security policies.
• Orchestrate model swaps and configuration changes in production, including rollout planning, risk assessment, change control, and post-deployment monitoring.
• Design, maintain, and govern shared prompt libraries, including standards for prompt quality, reuse, versioning, and training for end-users and builders.
• Own management of secrets (API keys, credentials, tokens) used by AI tools and orchestrators, ensuring secure storage, rotation, and access control in partnership with Security and IT.
• Define and maintain connectors and extensions (e.g., integrations into SaaS systems, data sources, and workflow tools) to ensure reliable, secure data access for AI workflows.
• Establish and operate auditability frameworks for AI tools, including logging, traceability of AI-assisted actions, and reporting for compliance and risk management.
• Lead AI governance practices for workplace AI (policies, guardrails, usage standards, approval workflows, exception processes) in partnership with Security, Legal, and HR.
• Partner with business solution and build teams to ensure their deliverables meet sustainment, observability, and governance requirements before moving to production.
• Define operational playbooks, SLAs, and incident response procedures for AI systems, including on-call patterns supported by contractors and platform specialists.

Director of AI Operations – Governance

Technical Recruiter

• Own the top of funnel: Partner closely with recruiters and hiring leaders to define target profiles and build deep, data-informed sourcing strategies across Electrical, Mechanical, Software, Embedded, AI/ML, Systems, Manufacturing, and other engineering disciplines.
• Map the market: Develop comprehensive talent landscapes by company, skillset, geography, and organizational structure; identify hidden talent pools, competitor insights, and emerging talent trends to inform recruiting strategy and workforce planning.
• Generate and engage: Use advanced Boolean search techniques, sourcing platforms, and creative sourcing channels (LinkedIn Recruiter, SeekOut, HireEZ, ClearanceJobs, GitHub, engineering communities, niche aerospace networks, conferences, referrals, and more) to identify, engage, and attract exceptional technical talent.
• Build pipelines proactively: Cultivate long-term relationships with passive, cleared, and highly specialized engineering talent to enable just-in-time hiring across strategic programs.
• Leverage AI to scale sourcing: Design, build, and continuously improve AI-powered sourcing workflows, research agents, and practical automations that increase recruiter productivity, accelerate candidate discovery, enhance market intelligence, and improve operational efficiency.
• Build repeatable sourcing systems: Use AI tools and workflow automation to create structured market maps, talent intelligence reports, outreach content, sourcing documentation, and scalable recruiting processes that can be leveraged across the Talent Acquisition organization.
• Be the sourcing SME: Serve as the subject matter expert on technical sourcing by coaching recruiters and hiring managers on market insights, sourcing strategy, talent availability, competitive intelligence, candidate engagement best practices, and emerging recruiting technologies.
• Partner for impact: Collaborate cross-functionally with Talent Acquisition Operations, Employer Brand, University Recruiting, and Engineering leaders to optimize recruiting strategies, outbound messaging, candidate experience, and operational excellence.
• Track and iterate: Use recruiting analytics, experimentation, and storytelling to measure sourcing effectiveness, optimize outreach strategies, improve conversion rates, and continuously evolve sourcing practices.