Embedded Machine Learning Internship Jobs in Acton, MA

Job Summary:
Goddard is a company focused on delivering outstanding solutions that positively impact lives through engineering and design. They are seeking a Senior Machine Learning Engineer to own the AI/ML foundation of their physical AI initiative, responsible for the full ML lifecycle and collaborating with various engineering teams to integrate AI capabilities into physical devices.
Responsibilities:
• Design and implement data pipelines for sensor data ingestion, preprocessing, labeling, and curation, ensuring data quality from collection through training.
• Train, evaluate, and iterate on ML models for applications including signal processing, anomaly detection, and physiological parameter estimation.
• Optimize models for deployment on edge and embedded targets, applying quantization, pruning, and distillation techniques to meet latency and memory constraints.
• Deploy models to constrained hardware using TFLite, ONNX, TensorRT, or equivalent runtimes, and validate end-to-end inference behavior on target devices.
• Collaborate with embedded software engineers to integrate ML inference into device firmware and software stacks, defining clear interfaces and performance contracts.
• Build and maintain MLOps infrastructure: experiment tracking, model versioning, automated evaluation pipelines, and CI/CD for models.
• Work with hardware and systems teams on sensor selection, data collection protocol design, and validation methodology.
• Document model development, training procedures, validation results, and known limitations to support regulatory submissions and internal quality systems.
• Design and execute rigorous model validation: statistical test set design, distributional shift analysis, out-of-distribution detection, and confidence calibration, particularly for safety-relevant outputs.
• Proactively identify data quality gaps, model failure modes, and deployment blockers before they reach production.
Qualifications:
Required:
• 5+ years in machine learning engineering or applied ML, with a demonstrated track record of shipping models to production environments.
• Strong proficiency in Python; hands-on experience with PyTorch or TensorFlow for model development and training.
• Demonstrated experience optimizing and deploying models to edge or resource constrained targets using TFLite, ONNX, CoreML, TensorRT, or equivalent.
• Experience building and maintaining time-series or sensor data pipelines, including preprocessing, feature engineering, and data quality validation.
• Working knowledge of quantization, pruning, knowledge distillation, and other techniques for reducing model footprint and inference latency.
• Proficiency with experiment tracking tools (MLflow, Weights & Biases, or equivalent), model registries, and automated evaluation and testing workflows.
• Solid fundamentals — Git, code review, unit testing, and CI/CD — applied consistently to ML code, not just application code.
• Demonstrated ability to work autonomously across hardware and software domains, translate model behavior and limitations clearly to non-ML engineers, and surface risks and uncertainties early rather than at integration time.
• Working proficiency in C or C++ sufficient to read, review, and meaningfully collaborate on embedded inference integration code; ability to reason about memory layout, execution constraints, and cross-language interface boundaries.
• Bachelor's degree in Computer Science, Electrical Engineering, Applied Mathematics, Data Science, or a related field required.
Preferred:
• Experience with physiological signal processing for medical or wearable applications (ECG, PPG, SpO2, NIBP, IMU, or similar sensor modalities).
• Familiarity with FDA guidance on AI/ML-based Software as a Medical Device (SaMD) or practical experience developing software under IEC 62304.
• Background in robotics or autonomous systems, including sensor fusion, perception, or closed-loop control.
• Experience in a startup or small-team environment where scope, tooling, and process are built alongside the product.
• Advanced degree is a plus but not a substitute for hands-on experience shipping models to real systems.
Company:
Goddard specializes in the design and development of medical technology, life science and diagnostics. Founded in 1997, the company is headquartered in Beverly, USA, with a team of 51-200 employees. The company is currently Growth Stage.