{"id":2638381,"url":"https://alion.io/job/anduril-industries-senior-machine-learning-engineer-applied-intelligence-2","title":"Senior Machine Learning Engineer (Applied Intelligence)","company":{"id":14,"name":"Anduril Industries","domain":"anduril.com","url":"https://alion.io/company/anduril","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":80,"open_postings":587,"ghost_share":0,"stale_share":0.785,"repost_share":0,"time_to_fill_p50_days":53,"computed_at":"2026-10-23T05:54:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Ana, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":220000,"max":292000,"currency":"USD","period":"year","gross":null,"usd_annual":292000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Sensor Fusion","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"Feast","optional":true},{"name":"Flyte","optional":true},{"name":"Kubeflow","optional":true},{"name":"LLM","optional":true},{"name":"Milvus","optional":true},{"name":"MLFlow","optional":true},{"name":"OCR","optional":true},{"name":"Pinecone","optional":true},{"name":"Quantization","optional":true},{"name":"RAG","optional":true},{"name":"TensorRT","optional":true},{"name":"Triton Inference Server","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-15T15:44:33Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-23T03:36:56Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\nABOUT THE TEAM\nMaritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. We build and deploy the full technology stack that powers Anduril's shipbuilding factories: the data infrastructure that makes every machine and sensor visible in real time, the manufacturing execution system (ArsenalOS) that workers and planners use every shift, the scheduling engine that replans production in minutes instead of days, and the AI systems that eliminate manual toil from both the shop floor and business operations.\nMDP operates at the boundary between Operational Technology and Information Technology. Our systems live where factory-floor machines, edge compute, and OT networks meet enterprise platforms and cloud infrastructure. We incubate solutions close to the production line, validate them with real operators building real hardware, harden them for reliability and security, and then scale them across multiple sites. The environment is fast, physical, and consequential. When our systems go down, production stops. The output of our work is not a dashboard: it is a ship.\nThis is not a support function. It is a strategic investment by Anduril in the premise that digitizing the manufacturing lifecycle end-to-end, from engineering definition through scheduling through execution through field feedback, is how Heavy Metal will out-build, out-adapt, and out-scale the traditional defense industrial base. MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites. You will be joining early, working on hard problems with real operational stakes, and shaping how manufacturing software is built at Anduril from the ground up.\nABOUT THE JOB\nAs a Machine Learning Engineer on the Applied Intelligence initiative, you will be the critical bridge between ML research and production operations. You will own the infrastructure that brings ML prototypes from concept to reliable, production-grade systems running on factory floors and serving manufacturing teams. Working at the intersection of data engineering, MLOps, and production operations, you'll execute the vision of ML engineers by building robust datasets, training infrastructure, and inference systems that operate reliably in both cloud and on-premises OT environments.\nThis role requires someone who can translate ML requirements into production infrastructure, coordinate across teams (data engineering, Big Anduril, manufacturing operations), and serve as the on-call owner for deployed ML systems. You'll be responsible for ensuring that AI applications don't just work in demos-they work 24/7 in real manufacturing environments where downtime has real consequences.\nWHAT YOU'LL DO\nInfrastructure Ownership & Production Operations\nOwn production hardening and deployment of ML prototype applications, transforming research concepts into reliable, maintainable production systems\nTake part in on-call rotations for deployed ML products running on factory floors and serving Mantech, ensuring high availability and rapid incident response\nBuild and maintain inference infrastructure across cloud environments and on-premises OT hardware deployed on the factory floor\nDeploy and operate ML systems in factory environments, including edge compute clusters and OT networks, with a focus on reliability and observability\nImplement comprehensive monitoring, alerting, and drift detection systems to maintain operational SLOs for production ML applications\nDefine and execute rollback procedures, validation gates, and safety reviews for AI systems in production\nCross-Functional Coordination & Requirements Translation\nInterface directly with the data engineering team to define and deliver ML-specific data infrastructure needs for Applied Intelligence\nCoordinate with Anduril enterprise teams to secure compute resources, labeling infrastructure, data access, and modeling capabilities needed for ML initiatives\nExecute the technical vision of ML engineers by translating their requirements into concrete infrastructure for datasets, training pipelines, and inference systems\nPartner with manufacturing engineers and factory operators to understand production workflows and translate them into MLOps requirements\nDrive make/buy decisions by evaluating internal and vendor capabilities against Applied Intelligence needs\nPlatform & Infrastructure Development\nArchitect and own the end-to-end ML platform stack-from data ingestion and labeling through training orchestration to model serving and lifecycle management\nBuild model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems\nImplement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from factory sensors and production systems\nSelect, prioritize, and standardize MLOps components including feature stores, vector databases, model registries, orchestration layers, and observability systems\nDesign training infrastructure that enables ML engineers to iterate quickly while maintaining reproducibility and governance\nData & Model Lifecycle Management\nOwn dataset creation, curation, versioning, and management processes that enable high-quality model development\nImplement model governance processes for validation, traceability, retraining triggers, and version control\nBuild systems for data labeling workflows, quality assurance, and continuous data pipeline monitoring\nManage model versioning, experiment tracking, and artifact management across development and production environments\nCode Quality & Team Practices\nWrite production-quality code with comprehensive tests, participating in code review and architectural discussions\nEstablish best practices for MLOps, observability, data management, and secure handling of sensitive production data\nLeverage AI tooling (coding assistants, automation) in your development workflow and contribute to team engineering practices\nMentor junior engineers and data scientists on production ML best practices\nREQUIRED QUALIFICATIONS\n6+ years of experience in software engineering or ML engineering roles building production systems, ideally in fast-paced environments\nStrong production MLOps experience: data pipeline management, model versioning, deployment automation, and production monitoring\nProven track record of taking ML models from prototype to production and maintaining them long-term\nExperience with on-call rotations and production incident management for data or ML systems\nStrong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow)\nExperience building and deploying containerized ML services using Docker and Kubernetes\nSolid data engineering skills including pipeline orchestration, data quality management, and working with time-series or event-driven data\nExperience implementing observability for model performance, inference accuracy, and data drift\nStrong technical communication skills with ability to work across ML research, data engineering, and operations teams\nExperience building systems that must operate reliably under real-world operational constraints (high availability, low latency, constrained environments)\nProven ability to coordinate across multiple teams and translate requirements into technical solutions\nDegree in Computer Science, Engineering, or related technical field, or equivalent practical experience\nU.S. Person status is required as this position needs to access export controlled data\nPREFERRED QUALIFICATIONS\nExperience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, factory automation, IoT)\nBackground deploying ML models to edge devices or on-premises hardware in production environments\nExperience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal\nFamiliarity with GPU infrastructure management and inference optimization (TensorRT, Triton Inference Server)\nExperience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries\nExperience building RAG systems, vector databases (Pinecone, Weaviate, Milvus), or LLM deployment pipelines\nBackground with computer vision model deployment, OCR/IDP systems, or document processing pipelines\nExperience coordinating with platform or infrastructure teams at larger organizations (e.g., working with central ML platform teams)\nFamiliarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration\nKnowledge of edge ML deployment, model optimization (quantization, pruning), or deploying models on resource-constrained devices\nExperience in regulated environments (NNPI/ITAR) and secure model/data governance\nExperience in hyper-growth startup environments, with demonstrated success balancing speed, ambiguity, and long-term system health\nFamiliarity with enterprise systems such as ERP, MES, WMS, or PLM\nEligible to obtain and maintain a U.S. Secret security clearance\nUS Salary Range\n$220,000—$292,000 USD\nThe salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: \nBenefits\nAt Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits.\nProtecting Yourself from Recruitment Scams\nAnduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.\nTo ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind:\nNo Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates.\n\nPlease always verify communications:Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address.\nVia Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to . \n\nExercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the se...","description_format":"text","description_chars":13241,"description_truncated":true,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":true,"languages":[]},"benefits":["Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["C4ISR","Military Drones","Defense Manufacturing","Defense AI"],"lifecycle":[{"event":"open","at":"2026-10-15T17:58:40Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":7,"expected_fill_days":53,"reasons":["conf:2","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-23T05:54:15Z"},"pay":{"stated_usd_annual":292000,"is_top_pay":true},"html_url":"https://alion.io/job/anduril-industries-senior-machine-learning-engineer-applied-intelligence-2","json_url":"https://alion.io/job/anduril-industries-senior-machine-learning-engineer-applied-intelligence-2.json","meta":{"generated_at":"2026-10-23T06:28:16Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"search","counted_by":"address","units_charged":0,"used_today":0,"day_limit":null,"remaining_today":null,"minute_limit":null,"resets_at":"2026-10-24T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":14},"rest":"https://alion.io/mcp/rest/get_company?id=14"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fanduril-industries-senior-machine-learning-engineer-applied-intelligence-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fanduril-industries-senior-machine-learning-engineer-applied-intelligence-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fanduril-industries-senior-machine-learning-engineer-applied-intelligence-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/anduril-industries-senior-machine-learning-engineer-applied-intelligence-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fanduril-industries-senior-machine-learning-engineer-applied-intelligence-2"}]}