Shieldai

<div><span>Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit </span><a rel="noopener noreferrer ugc" href="https://shield.ai/" target="_blank"><span>www.shield.ai</span></a><span>. Follow Shield AI on </span><a rel="noopener noreferrer ugc" href="https://www.linkedin.com/company/shield-ai" target="_blank"><span>LinkedIn</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://x.com/shieldaitech" target="_blank"><span>X</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://www.instagram.com/shield_ai" target="_blank"><span>Instagram</span></a><span>, and </span><a rel="noopener noreferrer ugc" href="https://www.youtube.com/@Shield_AI" target="_blank"><span>YouTube</span></a><span>.</span><span> </span></div>
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<div>Our Summer Internship program is a wonderful opportunity for Fall and Spring graduates to immerse themselves in an innovative and mission driven culture. Our paid internship program will provide you with hands on mentorship, an opportunity to add real contribution to our company via your internship project, and a possible job offer at the end. You will have the chance to make lifelong friendships, expand your network, and learn all about what it's like to work in a high-growth, fast scaling tech unicorn. You'll take part in company sponsored events and enjoy exploring a new city. Read on below to learn more about this unique internship!</div><div><br /></div><div>Shield AI is seeking a Data Science Intern to assess data readiness and develop predictive forecasting capabilities for Anaplan. Working with Finance and engineering mentors, you will build and evaluate models, then prepare their outputs for integration into existing planning models. Your work will advance the vision of an autonomous, continuously updated forecast that reduces preparation effort and gives the executive team and board access to current, governed projections.<br /><br /></div>
<h2>What you'll do</h2><ul><li>Assess financial and operational data for predictive readiness, identifying useful drivers and material gaps.</li>
<li>Build baseline and machine-learning forecasts for a focused Finance use case.</li>
<li>Evaluate accuracy, stability, and explainability using appropriate backtesting.</li>
<li>Prepare model outputs for Anaplan consumption and demonstrate an integration prototype with the team.</li>
<li>Recommend the data, monitoring, and review practices needed to support reliable forecast updates.</li>
<li>Present findings and deliver documented, reproducible work that informs the next stage of development.<br /><br /></li></ul>
<h2>Required qualifications</h2><ul><li>Pursuing a Bachelor's or Master's degree in data science, statistics, computer science, applied mathematics, economics, or a related field.</li>
<li>Coursework or project experience with Python, SQL, statistics, and predictive modeling.</li>
<li>Ability to assess data quality and communicate model assumptions and limitations.<br /><br /></li></ul>
<h2>Preferred qualifications</h2><ul><li>Experience with time-series forecasting, feature engineering, or model evaluation.</li>
<li>Familiarity with Databricks, financial data, or enterprise planning.</li>
<li>Interest in integrating machine learning into practical business workflows.</li>
<li>Strong curiosity, collaboration, and problem-solving skills.</li></ul>

Summer 2027 - Data Science Intern (R6324)

<div><span>Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit </span><a rel="noopener noreferrer ugc" href="https://shield.ai/" target="_blank"><span>www.shield.ai</span></a><span>. Follow Shield AI on </span><a rel="noopener noreferrer ugc" href="https://www.linkedin.com/company/shield-ai" target="_blank"><span>LinkedIn</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://x.com/shieldaitech" target="_blank"><span>X</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://www.instagram.com/shield_ai" target="_blank"><span>Instagram</span></a><span>, and </span><a rel="noopener noreferrer ugc" href="https://www.youtube.com/@Shield_AI" target="_blank"><span>YouTube</span></a><span>.</span><span> </span></div><div><br /></div><h3>Job Description:</h3>
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<p>Shield AI is seeking a Data Engineer to build the reliable, governed data foundation for Finance's next generation of planning and decision support. Working on our Databricks platform, you will own reusable integrations, semantic-layer infrastructure, and production data services that support analytics and AI-enabled applications. You will combine engineering rigor with AI-assisted development to increase delivery speed, reliability, and cost efficiency.</p>
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<h2>What you'll do:</h2><div>

<li>Build and operate scalable pipelines and integrations connecting enterprise data to Finance products.</li>
<li>Develop semantic-layer infrastructure and governed data access for analytics, planning, and AI applications.</li>
<li>Establish end-to-end pipeline observability, data-quality controls, and resilient operations.</li>
<li>Engineer the services and integrations behind Databricks Genie and other AI-enabled Finance workflows.</li>
<li>Use AI coding assistants to accelerate development while validating correctness, security, and performance.</li>
<li>Apply FinOps practices to manage platform spend and optimize cost without compromising service quality.</li>
<li>Partner with analytics engineers and enterprise teams on architecture, data contracts, and sustainable ownership.</li>

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<h2>Required qualifications:</h2><div>

<li>Bachelor’s degree in computer science or a related technical or quantitative field and a minimum of 2 years of related experience.</li>
<li>Hands-on production data engineering experience with SQL and Python or PySpark.</li>
<li>Experience with cloud data platforms, orchestration, data modeling, and governed serving patterns.</li>
<li>Ability to design for observability, recoverability, security, and maintainability.</li>
<li>Proficiency with AI coding assistants and independent validation of generated work.</li>
<li>Practical experience enabling AI capabilities on enterprise data, ideally in Databricks; adjacent Snowflake Cortex or comparable experience is welcome.</li>
<li>Strong cross-functional collaboration and production ownership.</li>

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<h2>Preferred qualifications:</h2><div>

<li>Experience with Databricks, Delta Lake, cloud infrastructure, and deployment automation.</li>
<li>Experience developing AI application backends, model integrations, or LLM-based workflows.</li>
<li>Experience with FinOps, financial data, or enterprise-system integrations.</li>

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Data Engineer, Finance (R6329)

<div><span>Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit </span><a rel="noopener noreferrer ugc" href="https://shield.ai/" target="_blank"><span>www.shield.ai</span></a><span>. Follow Shield AI on </span><a rel="noopener noreferrer ugc" href="https://www.linkedin.com/company/shield-ai" target="_blank"><span>LinkedIn</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://x.com/shieldaitech" target="_blank"><span>X</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://www.instagram.com/shield_ai" target="_blank"><span>Instagram</span></a><span>, and </span><a rel="noopener noreferrer ugc" href="https://www.youtube.com/@Shield_AI" target="_blank"><span>YouTube</span></a><span>.</span><span> </span></div><div><br /></div><h3>Job Description:</h3>
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<p>The Staff Industrial Engineer, Sustainment Analytics, leads data-driven sustainment decisions by integrating maintenance, FRACAS, failure and repair, supply-chain, operational demand, configuration, labor, and fleet-trend data to identify drivers of downtime, maintenance burden, readiness risk, and lifecycle cost. The role translates these insights into structured analyses, defensible trade studies, and actionable recommendations that optimize maintenance levels, repair capability, spares, manpower, support equipment, processes, and sustainment strategy—improving fleet readiness, repair throughput, maintenance effectiveness, and total cost of ownership.</p>
<p>The engineer partners closely with Fleet Support Engineering, Reliability and Maintainability Engineering, Product Engineering, Reliability Data Engineering, Supply Chain, Manufacturing, Finance, and Operations to ensure sustainment decisions improve fleet availability, customer support, affordability, and long-term system performance.</p>
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<h2>What you'll do:</h2><div>

<li>Own sustainment decision analysis for fielded aircraft systems, including maintenance-level determinations; repair-versus-replace, repair-location, and repair-capability assessments; spares optimization; manpower analysis; and support-equipment trade studies.</li>
<li>Build and maintain integrated fleet-readiness and availability models that link reliability, maintainability, maintenance demand, repair turnaround time, inventory and supply lead times, labor availability, operational utilization, repair capacity, and cost to readiness outcomes.</li>
<li>Develop repair-versus-replace and repair-network business cases that assess discard, organizational/field-level repair, depot or centralized repair, supplier return, and redesign alternatives using repair yield, turnaround time, labor, test-equipment requirements, supply lead times, fleet impact, risk, and lifecycle cost.</li>
<li>Analyze maintenance records, work orders, failure and repair history, supply and logistics data, labor data, and fleet operations data to identify recurring maintenance burdens, demand drivers, capacity constraints, bottlenecks, readiness risks, and high-value sustainment opportunities.</li>
<li>Develop demand forecasts, spares recommendations, and provisioning strategies using failure rates, consumption history, repair turnaround times, operational tempo, lead times, service-level targets, and deployment requirements.</li>
<li>Perform manpower, workload, capacity, and skill-mix analyses to define staffing needs, identify workload constraints and maintenance bottlenecks, and improve repair throughput and maintenance effectiveness.</li>
<li>Evaluate investments in support equipment, test equipment, tooling, facilities, and repair capability by quantifying capacity, utilization, cost, risk reduction, readiness impact, return on investment, and total lifecycle value.</li>
<li>Develop lifecycle-cost models and economic trade studies to inform sustainment planning, maintenance-program changes, provisioning decisions, repair-network strategy, and leadership investment decisions.</li>
<li>Apply Pareto, trend, statistical, sensitivity, and scenario analyses to prioritize actions that improve fleet availability, reduce downtime, lower sustainment cost, and mitigate readiness risk.</li>
<li>Translate complex analyses into clear recommendations, decision packages, executive-ready briefings, and prioritized action plans for sustainment leadership and cross-functional stakeholders.</li>
<li>Establish repeatable sustainment analytics processes, including data standards, modeling methods, decision criteria, assumptions management, and lessons-learned feedback loops; mature the capability toward predictive maintenance, condition-based maintenance, and proactive fleet-health decision support.</li>

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<h2>Required qualifications:</h2><div>

<li>Bachelor’s degree in Industrial Engineering, Systems Engineering, Operations Research, Data Analytics, or a related technical discipline; equivalent practical experience considered.</li>
<li>5+ years of experience in industrial engineering, sustainment, logistics, supportability, maintenance, operations research, fleet operations, aviation sustainment, defense logistics, or other complex hardware-support environments.</li>
<li>Demonstrated experience using maintenance, reliability, repair, inventory, supply-chain, production, fleet-operations, or fielded-hardware data to develop quantitative analyses and recommendations related to availability, readiness, repair capability, logistics, labor, capacity, cost, and operational performance.</li>
<li>Experience performing repair-versus-replace, make-versus-buy, repair-capability, or comparable sustainment trade studies; able to build structured models, quantify assumptions, conduct sensitivity analyses, compare alternatives, and communicate uncertainty and risk.</li>
<li>Proficiency with SQL, Python, Power BI, Tableau, Excel, R, MATLAB, or comparable tools for data analysis, modeling, visualization, and reporting.</li>
<li>Ability to integrate technical, operational, supply-chain, labor, and financial inputs into practical, data-driven recommendations for complex sustainment decisions while balancing analytical rigor, data limitations, operational urgency, customer impact, fleet readiness, cost, and long-term sustainment needs.</li>
<li>Strong cross-functional collaboration and communication skills, including the ability to work with engineering, fleet support, supply chain, manufacturing, finance, operations, and analytics teams; develop clear decision packages, technical analyses, recommendations, and leadership-level summaries; and independently drive decisions without direct authority.</li>

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<h2>Preferred qualifications:</h2><div>

<li>Familiarity with aviation, defense, aerospace manufacturing, unmanned systems, aircraft sustainment and deployed hardware systems.</li>
<li>Familiarity with RCCA, FRACAS, PQDR, AS9100, service bulletins, maintenance releases, supply chain and workforce management.</li>
<li>Proficiency with data analysis and visualization tools such as SQL, Excel, Python, Salesforce, Foundry, or similar platforms.</li>
<li>Demonstrated ability to collect, clean, join, and structure data from multiple operational, maintenance, failure, or quality systems.</li>
<li>Experience developing metrics, dashboards, or recurring reports that improved decision-making, increased visibility, or reduced manual reporting effort.</li>
<li>Demonstrated success identifying and correcting data-quality, traceability, or reporting issues before they affected technical or business decisions.</li>
<li>Ability to translate complex data into clear, decision-ready information for technical and non-technical audiences.</li>
<li>Experience supporting military, government, international, or deployed aviation customers.</li>
<li>Familiarity with ITAR, export-controlled technical data, or controlled customer environments.</li>
<li>Active Secret or Top Secret clearance.</li>

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Staff Engineer, Industrial (R6313)

<div><span>Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit </span><a rel="noopener noreferrer ugc" href="https://shield.ai/" target="_blank"><span>www.shield.ai</span></a><span>. Follow Shield AI on </span><a rel="noopener noreferrer ugc" href="https://www.linkedin.com/company/shield-ai" target="_blank"><span>LinkedIn</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://x.com/shieldaitech" target="_blank"><span>X</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://www.instagram.com/shield_ai" target="_blank"><span>Instagram</span></a><span>, and </span><a rel="noopener noreferrer ugc" href="https://www.youtube.com/@Shield_AI" target="_blank"><span>YouTube</span></a><span>.</span><span> </span></div><div><br /></div><h3>Job Description:</h3>
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<p>Help shape the future of physical AI in defense by building reasoning models for unmanned autonomous systems across aerial, maritime, land, and space domains. </p>
<p>We’re looking for a deeply technical engineer who can turn emerging research into reliable autonomous capabilities and shares our conviction that AI can protect service members and civilians. As a Staff Engineer on the Hivemind Reasoning team, you’ll drive the design, training, adaptation, and evaluation of reasoning and cognition models intended for deployment on unmanned platforms. </p>
<p>You’ll take hands-on ownership of complex technical problems, from defining model architectures and training objectives to establishing rigorous evidence of mission effectiveness. Your work will support individual vehicles and coordinated teams of hundreds of agents, while accounting for limited compute, intermittent communications, incomplete information, and other constraints of operating at the edge in contested environments. </p>
<p>You’ll partner closely with teams in perception, MLOps, large-scale simulation, autonomy, and inference to deliver capabilities as part of Hivemind Enterprise.  </p>
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<h2>What you'll do:</h2><div>

<li>Design, implement, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous platforms. </li>
<li>Make and validate tradeoffs among foundation-model approaches, including vision-language models (VLMs) and vision-language-action models (VLAs), specialized deep-learning models, and traditional AI/ML methods. </li>
<li>Develop capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information. </li>
<li>Build reproducible training and adaptation workflows using appropriate combinations of self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation. </li>
<li>Define data requirements, training objectives, benchmarks, and evaluation methods that support meaningful comparisons among model approaches. </li>
<li>Establish rigorous evaluations in simulation and with real-world data, measuring generalization, robustness, uncertainty, and performance as team size and operating conditions change. </li>
<li>Incorporate edge constraints into model architecture and training decisions in partnership with hardware, autonomy, and inference teams. </li>
<li>Diagnose model failures and create feedback loops among data curation, training experiments, and re-evaluation. </li>
<li>Deliver trained models, reproducible results, and clear performance evidence to partner teams. </li>
<li>Communicate technical tradeoffs, experimental findings, and model limitations clearly to research, engineering, product, and organizational leadership. </li>

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<h2>Required qualifications:</h2><div>

<li>BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience. Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience. </li>
<li>A demonstrated track record of developing and deploying advanced ML models in production environments, with substantial hands-on contributions to model design, training, and evaluation. </li>
<li>Deep expertise in foundation models, modern deep learning, model architectures, and the training ecosystem. </li>
<li>Strong command of training and adaptation approaches such as self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation. </li>
<li>Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible criteria for comparing models.</li>
<li>Fluency in traditional AI/ML techniques and an ability to combine them thoughtfully with learned models. </li>
<li>Experience with training frameworks such as PyTorch, JAX, TensorFlow, etc.</li>
<li>Experience with multimodal, embodied, robotic, or autonomous-system models; familiarity with VLA models and world models is particularly relevant. </li>
<li>Familiarity with traditional robotics algorithms, e.g. path planning, probabilistic modeling, optimization approaches, etc.</li>
<li>Familiarity with NVIDIA Jetson and other edge compute platforms (e.g. Qualcomm, AMD, etc.).</li>

</div>
<h2>Preferred qualifications:</h2><div>

<li>MS or PhD in Computer Science, Electrical Engineering, or similar degree. </li>
<li>Expertise in optimising and deploying ML models to resource-constrained hardware platforms. </li>
<li>Experience with simulation-to-real on autonomous systems. </li>
<li>Experience with unmanned autonomous systems in defense applications.</li>
<li>Familiarity with large, distributed systems.</li>
<li>Familiarity with high-fidelity simulation, e.g. rigid body dynamics, sensor &amp; actuator models, etc. </li>

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Staff Engineer, Hivemind Reasoning (R6292)

<div><span>Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit </span><a rel="noopener noreferrer ugc" href="https://shield.ai/" target="_blank"><span>www.shield.ai</span></a><span>. Follow Shield AI on </span><a rel="noopener noreferrer ugc" href="https://www.linkedin.com/company/shield-ai" target="_blank"><span>LinkedIn</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://x.com/shieldaitech" target="_blank"><span>X</span></a><span>, </span><a rel="noopener noreferrer ugc" href="https://www.instagram.com/shield_ai" target="_blank"><span>Instagram</span></a><span>, and </span><a rel="noopener noreferrer ugc" href="https://www.youtube.com/@Shield_AI" target="_blank"><span>YouTube</span></a><span>.</span><span> </span></div><div><br /></div><h3>Job Description:</h3>
<div>Shield AI is seeking a Senior Analytics Engineer to lead the design and delivery of trusted data products for Corporate FP&amp;A, Accounting, Program Finance, GTM, and Strategic Finance. You will independently shape analytical solutions, establish shared standards, and turn business priorities into AI-enabled decision support on our Databricks platform. This senior individual contributor role combines hands-on engineering with cross-functional influence and mentorship to expand Finance's impact.</div>
<h2>What you'll do:</h2><div>

<li>Shape the analytics roadmap with Finance and GTM leaders, prioritizing high-impact products and resolving competing requirements.</li>
<li>Build governed datasets and semantic models that support reporting, planning, scenario analysis, and strategic decisions.</li>
<li>Set modeling and metric standards across domains, exercising judgment on architecture, quality, and maintainability.</li>
<li>Develop conversational analytics and AI-enabled workflows, including Databricks Genie, grounded in trusted business context.</li>
<li>Use AI coding assistants to accelerate delivery and explore new capabilities while maintaining rigorous validation and quality.</li>
<li>Own business-facing reconciliation and acceptance, partnering with data engineering on reliable delivery.</li>
<li>Mentor engineers and Finance partners, strengthening technical practices and self-service capabilities.</li>

</div>
<h2>Required qualifications:</h2><div>

<li>Substantial experience in analytics engineering, BI engineering, or financial data modeling, with advanced SQL and a record of delivering production data products.</li>
<li>Demonstrated ability to lead ambiguous, cross-functional work from problem definition through adoption.</li>
<li>Proficiency with AI coding assistants and sound judgment when reviewing generated work.</li>
<li>Practical experience with AI-assisted analytics, ideally Databricks Genie; adjacent Snowflake Cortex or comparable experience is welcome.</li>
<li>Sound architectural judgment, disciplined development practices, and experience mentoring others and influencing senior stakeholders.</li>

</div>
<h2>Preferred qualifications:</h2><div>

<li>Experience with Databricks, Python, semantic layers, or AI application development.</li>
<li>Experience supporting FP&amp;A, GTM, Strategic Finance, Accounting, or Program Finance.</li>
<li>Familiarity with enterprise planning, financial controls, or high-growth business environments.</li>

</div>

Shieldai

Open jobs at Shieldai

Summer 2027 - Data Science Intern (R6324)

Data Engineer, Finance (R6329)

Staff Engineer, Industrial (R6313)

Staff Engineer, Hivemind Reasoning (R6292)

Senior Analytics Engineer - Finance (R6185)

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