{"id":926556,"url":"https://alion.io/job/mach-industries-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":4935,"name":"Mach Industries","domain":"machindustries.com","url":"https://alion.io/company/machindustries","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Huntington Beach, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":120000,"max":160000,"currency":"USD","period":"year","gross":null,"usd_annual":160000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"C++","optional":false},{"name":"CNN","optional":false},{"name":"DVC","optional":false},{"name":"Edge AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"Sensor Fusion","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TensorRT","optional":false},{"name":"Transformers","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"Docker","optional":true},{"name":"Multimodal AI","optional":true},{"name":"ROS","optional":true},{"name":"Rust","optional":true}],"status":"live","first_seen_at":"2026-09-02T14:28:41Z","employer_posted_date":"2026-09-02","last_verified_at":"2026-10-16T21:38:27Z","board_verified":true,"closed_at":null,"days_open":44,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":44},"description":"About Mach Industries\nFounded in 2023, Mach Industries is a rapidly growing defense technology company focused on developing next-generation autonomous defense platforms. At the core of our mission is the commitment to delivering scalable, decentralized defense systems that enhance the strategic capabilities of the United States and its allies. With a workforce of approximately 350 employees, we operate with startup agility and ambition.\nOur vision is to redefine the future of warfare through cutting-edge manufacturing, innovation at speed, and unwavering focus on national security. We are dedicated to solving the next generation of warfare with lethal systems that deter kinetic conflict and protect global security.\nThe Role\nMach Industries is building an AI-forward autonomy stack for contested environments where GPS and other sensing are unavailable or unreliable. As a Machine Learning Engineer, you will own and scale the training, data, and edge-inference backbone that every vision and multi-sensor model on our product lines depends on for detection, tracking, search, navigation, targeting, and automatic target recognition. This is a broad, high-ownership role: you'll stand up the data and training infrastructure that lets the autonomy team iterate fast, generate synthetic data to cover the long tail, and get research-grade models running in real time on embedded hardware in flight. We are generalists, so you'll move fluidly between infrastructure, modeling, and deployment.\nKey Responsibilities\nOwn and evolve the training and data infrastructure the autonomy team builds on: ingestion from flight/sim/HITL, curation and mining, labeling/QA workflows, dataset versioning (DVC/Parquet), and reproducible dataset builds.\n\nStand up and scale training/eval infrastructure: distributed multi-GPU training, experiment tracking, a model registry, and CI-based evaluation with regression gates plus automated field-data to retrain to validate to redeploy loops.\n\nDeploy and optimize models for real-time edge inference on Jetson-class hardware (quantization/pruning, TensorRT/ONNX Runtime); profile CPU/GPU and hit tight latency, throughput, and SWaP targets.\n\nBuild and improve models across the portfolio as a hands-on IC: detection, segmentation, tracking, target/area search, classification/ATR, and multi-sensor fusion for EO/IR and auxiliary sensing.\n\nGenerate and manage synthetic data at scale (simulation + domain randomization) to cover long-tail and degraded conditions and close sim-to-real gaps.\n\nInstrument runtime health, drift detection, and graceful degradation, and wire model-performance metrics back into the data and retraining loop.\n\nLive close to flight data with visualization, triage, and root-cause tooling so the team can go from field logs to insight and model updates rapidly.\n\nPartner with other autonomy disciplines across perception, localization, embedded, and flight-test to take capabilities from prototype to sim to HITL to flight to deployment.\n\nRequired Qualifications\nStrong generalist software engineering: Python for ML and tooling, plus production C++ on Linux; profiling, optimization, and rigorous testing discipline.\n\nProven experience building ML data and training pipelines end to end: dataset construction, labeling/QA, augmentation, experiment tracking, and reproducible training.\n\nHands-on training and fine-tuning in PyTorch across modern detection/segmentation/tracking architectures (CNN/Transformer).\n\nEdge and real-time deployment: model compression (INT8/FP16), runtime optimization (TensorRT/ONNX Runtime), and meeting latency/SWaP constraints on embedded GPU (Jetson-class) hardware.\n\nData and MLOps infrastructure: SQL/Parquet, dataset/versioning tools, CI-based validation, and scalable multi-GPU training.\n\nBS/MS/PhD in CS/EE/Robotics or similar, or equivalent experience, with a track record shipping ML models to production or hardware. Senior candidates: deeper ownership of training/data infrastructure at scale.\n\nPreferred Qualifications\nSynthetic data generation and simulation (e.g. Unreal/Isaac, domain randomization) and demonstrated sim-to-real transfer.\n\nEO/IR imagery experience and working with real flight/test data in challenging, degraded, or contested environments.\n\nMulti-modal perception and fusion (EO/IR + radar/LiDAR/RF) at the feature or decision level.\n\nDetection/tracking/search at scale; active learning and data-mining strategies for long-tail coverage.\n\nCUDA backends for performance debugging; ROS 2; NVIDIA Jetson deployment pipelines.\n\nDrift/dataset-shift monitoring, robustness and rare-event testing, long-horizon reliability metrics.\n\nDistributed training frameworks and cloud ML platforms (e.g. SageMaker); Docker for reproducibility; Rust for systems tooling.\n\nITAR Requirements\nTo conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here. \nEngineer\n$120,000—$160,000 USD\nSenior Engineer\n$165,000—$220,000 USD\nAt Mach, we believe that compensation is more than just a paycheck. It's a comprehensive package designed to reward the time, skills, and expertise you bring to our team. We are committed to offering competitive compensation that is equitable, transparent, and aligned with market trends.\nKey Elements of Our Compensation Philosophy:\nCompetitive Salaries: We continuously research market rates to ensure our salaries are competitive with those in our industry and geographic location.\nPerformance-Based Incentives: Our bonus structure is tied to both individual and company performance, creating a win-win scenario that motivates our team members to excel.\nEquity Participation: We offer stock options or equity to employees, ensuring that you share in the success and growth of the company.\nBenefit Packages: Our benefits include healthcare, dental and vision plans, retirement savings, and paid time off, because we understand that your well-being extends beyond the office.\nProfessional Development: We allocate funds for continuing education, training, and career growth.\nTransparency: We are committed to openness about our compensation tiers and how decisions are made, ensuring trust and mutual respect.\nBy joining Mach, you’re not just accepting a job-you’re becoming a key part of a team that values your contributions and invests in your growth. Together, we aim to create a rewarding environment that sets the stage for long-term success.\nData Privacy\nTo view Mach Industries candidate data privacy policy, please visit https://machindustries.com/privacy-policy.\nDisclosures\nThis position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license.\nMach participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.\nThe salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offers may vary based on (but not limited to) work experience, education and training, critical skills, and business considerations. Highly competitive equity grants are included in most offers and are considered part of Mach’s total compensation package. Mach offers benefits such as health insurance, retirement plans, and opportunities for professional development.\nMach is an equal opportunity employer committed to creating a diverse and inclusive workplace. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law, including those with a criminal history, in a manner consistent with the requirements of applicable state and local laws. 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