{"id":2652560,"url":"https://alion.io/job/shield-ai-senior-manager-engineering-hivemind-reasoning-r6291","title":"Senior Manager, Engineering - Hivemind Reasoning (R6291)","company":{"id":50,"name":"Shield AI","domain":"shield.ai","url":"https://alion.io/company/shield-ai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":73,"open_postings":91,"ghost_share":0,"stale_share":0.857,"repost_share":0.011,"time_to_fill_p50_days":65,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Management","role_family":"Management","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Mateo, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":290000,"max":430000,"currency":"USD","period":"year","gross":null,"usd_annual":430000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Imitation Learning","optional":false},{"name":"JAX","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Path Planning","optional":false},{"name":"Physical AI","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"SFT","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-10-15T19:35:43Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-11-03T07:03:35Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"Job Description:\nLead the future of physical AI in defense by building the reasoning models for unmanned autonomous systems, across aerial, maritime, land, and space domains.\nWe are looking for a leader with not only the deep technical expertise or the abilities to build a world-class team, but also someone with the strong conviction that AI will protect service members and civilians. This role is responsible for building and leading a team that researches and designs models, establishes training pipelines and rigorous evaluations, and ensures mission effectiveness at the edge under real-world constraints. These models must support individual vehicles and coordinated teams of hundreds of agents, while meeting the constraints of operating at the edge in contested environments.\nIn this role, you’ll partner closely with world-class teams in perception, MLOps, large-scale simulation, and inference to deliver capabilities as part of the broader Hivemind Enterprise team.\nWhat success looks like: your team’s reasoning models deliver exceptional outcomes for baseline platforms and missions, which can then be rapidly and confidently adapted to new platforms and missions by our solutions engineering organisation.\nWhat you'll do:\nBuild & lead the team that designs, trains, adapts, and evaluates reasoning and cognition models intended for deployment across our unmanned platforms. \nDefine the technical roadmap for single-agent and multi-agent models, including architecture choices, training methods, data strategy, and evaluation criteria. \nMake informed tradeoffs among foundation-model approaches (e.g. VLMs, VLAs), specialized deep-learning models, and traditional AI/ML methods. \nAdvance model capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information. \nDrive training efforts, including appropriate use of self-supervised learning, imitation learning, reinforcement learning, and distillation. \nEstablish rigorous evaluations in simulation and with real-world data. Measure generalization, robustness, uncertainty, and performance as team size and operating conditions change. \nRecruit, develop, and manage a high-performing team of AI/ML engineers. \nTranslate edge constraints into model-design and training decisions in partnership with hardware, autonomy, and inference teams. \nCommunicate technical tradeoffs and model limitations clearly to research, engineering, product, and organizational leadership. \nDeliver trained models and clear performance evidence to partner teams. \nCreate tight feedback loops among model failures, data curation, training experiments, and re-evaluation. \nRequired qualifications:\nBS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience. Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 8 years with a PhD; or equivalent work experience. \nExceptional track record of leading the development and deployment of state-of-the-art ML models in production environments, making difficult prioritisation decisions, and communicating tradeoffs across disciplines. \n5+ years of people management experience leading engineering or applied research teams, with demonstrated success hiring, coaching, developing technical leaders, and managing performance. \nDeep expertise in foundation models, modern deep learning, model architectures, and the training ecosystem. \nStrong command of training and adaptation approaches such as self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation. \nExperience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible criteria for comparing models. \nFluency in traditional AI/ML techniques and an ability to combine them thoughtfully with learned models. \nExperience with training frameworks such as PyTorch, JAX, TensorFlow, etc. \nExperience with multimodal, embodied, robotic, or autonomous-system models; familiarity with VLA models and world models is particularly relevant. \nStrong familiarity with traditional robotics algorithms, e.g. path planning, probabilistic modeling, optimization approaches, etc. \nFamiliarity with NVIDIA Jetson and other edge compute platforms (e.g. Qualcomm, AMD, etc.) \nPreferred qualifications:\nPhD in Computer Science, Electrical Engineering, or similar degree. \nExpertise in optimising and deploying ML models to resource-constrained hardware platforms. \nExperience with simulation-to-real on autonomous systems. \nExperience managing managers or leading multiple engineering teams. \nExperience with unmanned autonomous systems in defense applications \nFamiliarity with large, distributed systems \nFamiliarity with high-fidelity simulation, e.g. rigid body dynamics, sensor & actuator models, etc. \nExperience managing and interfacing with external partners and vendors","description_format":"text","description_chars":5027,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Military Drones","Defense Manufacturing","Defense AI","Drone AI"],"lifecycle":[{"event":"open","at":"2026-10-15T22:11:02Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 2","filings_12m":2,"filings_prev_12m":2,"green_card_filings_12m":0,"median_offered_wage_usd":132500,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA 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