Embedded Tinyml Jobs (NOW HIRING)

Job Summary:
E-Space is bridging Earth and space to enable hyper-scaled deployments of Internet of Things (IoT) solutions and services. As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, optimization, and deployment on embedded devices.
Responsibilities:
• Data Ingestion and Pipeline Development
• Design and build data ingestion pipelines from sensors including IMUs, accelerometers, gyroscopes, microphones, and other environmental sensors
• Handle raw sensor data: cleaning, labeling, synchronization, and storage
• Build tools to collect, version, and manage training datasets at scale
• Model Development and Training
• Develop and train ML models for classification, regression, anomaly detection, and signal processing tasks
• Select appropriate model architectures for each problem and hardware target
• Fine-tune pre-trained models for domain-specific tasks and data distributions
• Design and run experiments to evaluate and compare model performance
• TinyML and Embedded Deployment
• Optimize models for deployment on microcontrollers and edge processors such as ARM Cortex-M, RISC-V, and DSPs
• Apply quantization, pruning, and knowledge distillation to reduce model size and inference latency
• Use frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch
• Integrate ML inference into embedded firmware written in C, C++, or Rust
• Profile and optimize memory usage, power consumption, and real-time performance
• Hybrid LLM Integration
• Design hybrid architectures that combine on-device lightweight models with LLM-based reasoning
• Build pipelines that route tasks between edge inference and cloud or edge-hosted LLM components
• Evaluate trade-offs in latency, accuracy, and power between on-device and LLM-assisted approaches
• Software Embedding and Systems Integration
• Write clean, well-tested embedded software that integrates ML inference into real-time systems
• Work with RTOS environments such as FreeRTOS and Zephyr, as well as bare-metal firmware
• Collaborate with hardware and firmware teams to co-optimize the full system stack
• Documentation and Reporting
• Document design decisions, pipeline configurations, model benchmarks, and deployment procedures
• Prepare technical reports and presentations for internal teams and stakeholders
• Stay current with developments in TinyML, embedded AI, and edge computing and bring relevant innovations into the team
• Collaboration and Support
• Work closely with cross-functional teams including hardware engineers, firmware developers, and data scientists
• Provide technical support during hardware bring-up, system integration, and field testing
• Participate in design reviews and contribute constructive feedback across the stack
Qualifications:
Required:
• 2+ years of experience in machine learning engineering, with at least 2 years focused on embedded or edge ML
• Strong background in signal processing, sensor data handling, and real-time system constraints
• Hands-on experience with IMUs and other sensor types including accelerometers, gyroscopes, barometers, and microphones
• Proficiency in Python for ML development using frameworks such as PyTorch, TensorFlow, or scikit-learn
• Experience with C or C++ for embedded systems development
• Solid understanding of model optimization techniques including quantization, pruning, and distillation
• Experience deploying models with at least one embedded ML framework such as TFLite Micro, Edge Impulse, or ONNX Runtime
• Strong understanding of memory-constrained and power-constrained environments
• Excellent problem-solving skills and the ability to work independently and as part of a team
Preferred:
• 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
• Background in feature extraction techniques such as FFT, filter banks, and wavelet transforms
• Experience with hardware-aware neural architecture search or AutoML for edge targets
• Familiarity with Rust for embedded or systems programming
• Prior work on products in wearables, robotics, industrial sensing, or IoT
Company:
E-Space is bridging Earth & space with the most sustainable LEO space system, delivering real-time, anywhere comms, IoT & Smart-IoT services Founded in 2021, the company is headquartered in Toulouse, FRA, with a team of 201-500 employees. The company is currently Growth Stage.