AI / Embedded ML Engineer
Confirmed on the employer's own hiring board on Oct 8, 2026. First seen by Alion on Sep 4, 2026.
Join our team as an AI / Embedded ML Engineer, where you will be responsible for the full lifecycle of AI/machine learning on resource-constrained hardware. You will work closely with cross-functional teams, including hardware engineers, firmware developers, and data scientists, to deliver production-ready ML solutions on embedded devices. This position is based in Saratoga, CA.
Missions
- Conception et construction de pipelines d'ingestion de données à partir de capteurs, y compris le traitement des données brutes.
- Développement et formation de modèles d'apprentissage automatique pour des tâches de classification, de régression, de détection d'anomalies et de traitement du signal.
- Optimisation des modèles pour le déploiement sur des microcontrôleurs et des processeurs de périphérie, y compris l'application de techniques de quantification, de taille et de distillation des connaissances.
Profil recherché
- Strong background in signal processing, sensor data handling, and real-time system constraints- Solid understanding of model optimization techniques including quantization, pruning, and distillation
- Strong understanding of memory-constrained and power-constrained environments
- Proficiency in Python for ML development using frameworks such as PyTorch, TensorFlow, or scikit-learn
- Hands-on experience with IMUs and other sensor types including accelerometers, gyroscopes, barometers, and microphones
- Experience deploying models with at least one embedded ML framework such as TFLite Micro, Edge Impulse, or ONNX Runtime
- Experience with C or C++ for embedded systems development
- 2+ years of experience in machine learning engineering, with at least 2 years focused on embedded or edge ML
- Experience with RTOS platforms such as FreeRTOS or Zephyr
- Familiarity with MCU families including NXP, STM32, ESP32, or similar
- Experience designing hybrid edge-LLM pipelines or integrating small language models on device
- Experience with hardware-aware neural architecture search or AutoML for edge targets
- Background in feature extraction techniques such as FFT, filter banks, and wavelet transforms
- Familiarity with Rust for embedded or systems programming
- Prior work on products in wearables, robotics, industrial sensing, or IoT
- Excellent problem-solving skills and the ability to work independently and as part of a team