{"id":183679,"url":"https://alion.io/job/espace-ai-embedded-ml-engineer","title":"AI / Embedded ML Engineer","company":{"id":2438005,"name":"E-Space","domain":"e-space.com","url":"https://alion.io/company/e-space-com","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"C","score":69,"open_postings":105,"ghost_share":0.514,"stale_share":0,"repost_share":0.038,"time_to_fill_p50_days":52,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Saratoga, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":225000,"currency":"USD","period":"year","gross":null,"usd_annual":225000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"C++","optional":false},{"name":"Edge AI","optional":false},{"name":"ExecuTorch","optional":false},{"name":"FreeRTOS","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LiteRT","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","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":"RTOS","optional":false},{"name":"Rust","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"TFLite","optional":false}],"status":"live","first_seen_at":"2026-04-16T22:28:57Z","employer_posted_date":"2026-04-16","last_verified_at":"2026-10-19T10:03:09Z","board_verified":true,"closed_at":null,"days_open":185,"trust":{"level":"ghost","repost_count":1,"flags":["stale","company_stale"],"days_open":185},"description":"As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/ machine learning on resource-constrained hardware. This includes data ingestion, model development, optimization, and deployment on embedded devices. This role is critical for building reliable, low-power, real-time ML systems that operate at the edge.\nIn this role, you will leverage your expertise in sensor data processing, lightweight model design, embedded software, and hybrid LLM integration to deliver production-ready ML solutions on hardware.\nThis position will report to Head of Product Engineering, and you will work closely with hardware, firmware, software, and data teams. This position is based in Saratoga, CA.\nWhat you will do:\n\nData Ingestion and Pipeline Development\nDesign and build data ingestion pipelines from sensors including IMUs, accelerometers, gyroscopes, microphones, and other environmental sensors\nHandle raw sensor data: cleaning, labeling, synchronization, and storage\nBuild tools to collect, version, and manage training datasets at scale\n\nModel Development and Training\nDevelop and train ML models for classification, regression, anomaly detection, and signal processing tasks\nSelect appropriate model architectures for each problem and hardware target\nFine-tune pre-trained models for domain-specific tasks and data distributions\nDesign and run experiments to evaluate and compare model performance\n\nTinyML and Embedded Deployment\nOptimize models for deployment on microcontrollers and edge processors such as ARM Cortex-M, RISC-V, and DSPs\nApply quantization, pruning, and knowledge distillation to reduce model size and inference latency\nUse frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch\nIntegrate ML inference into embedded firmware written in C, C++, or Rust\nProfile and optimize memory usage, power consumption, and real-time performance\n\nHybrid LLM Integration\nDesign hybrid architectures that combine on-device lightweight models with LLM-based reasoning\nBuild pipelines that route tasks between edge inference and cloud or edge-hosted LLM components\nEvaluate trade-offs in latency, accuracy, and power between on-device and LLM-assisted approaches\n\nSoftware Embedding and Systems Integration\nWrite clean, well-tested embedded software that integrates ML inference into real-time systems\nWork with RTOS environments such as FreeRTOS and Zephyr, as well as bare-metal firmware\nCollaborate with hardware and firmware teams to co-optimize the full system stack\n\nDocumentation and Reporting\nDocument design decisions, pipeline configurations, model benchmarks, and deployment procedures\nPrepare technical reports and presentations for internal teams and stakeholders\nStay current with developments in TinyML, embedded AI, and edge computing and bring relevant innovations into the team\n\nCollaboration and Support\nWork closely with cross-functional teams including hardware engineers, firmware developers, and data scientists\nProvide technical support during hardware bring-up, system integration, and field testing\nParticipate in design reviews and contribute constructive feedback across the stack\n\nWhat you bring to this role:\n2+ years of experience in machine learning engineering, with at least 2 years focused on embedded or edge ML\nStrong background in signal processing, sensor data handling, and real-time system constraints\nHands-on experience with IMUs and other sensor types including accelerometers, gyroscopes, barometers, and microphones\nProficiency in Python for ML development using frameworks such as PyTorch, TensorFlow, or scikit-learn\nExperience with C or C++ for embedded systems development\nSolid understanding of model optimization techniques including quantization, pruning, and distillation\nExperience deploying models with at least one embedded ML framework such as TFLite Micro, Edge Impulse, or ONNX Runtime\nStrong understanding of memory-constrained and power-constrained environments\nExcellent problem-solving skills and the ability to work independently and as part of a team\n\nBonus points for the following:\nExperience with RTOS platforms such as FreeRTOS or Zephyr\nFamiliarity with MCU families including NXP, STM32, ESP32, or similar\nExperience designing hybrid edge-LLM pipelines or integrating small language models on device\nBackground in feature extraction techniques such as FFT, filter banks, and wavelet transforms\nExperience with hardware-aware neural architecture search or AutoML for edge targets\nFamiliarity with Rust for embedded or systems programming\nPrior work on products in wearables, robotics, industrial sensing, or IoT\n\nAdditional Requirements\nThis is a fast-paced, high-impact environment - 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