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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - Physical AI - **Company:** Goddard, Inc. - **Location:** Wilmington, MA, United States - **Experience:** Expert - **Salary:** $140,000.0 - $165,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Systems Engineering, Unit Testing, C++ (Programming Language), Program Optimization, Code Review, Communications Protocols, Computer Programming, Continuous Integration, Information Engineering, Data Transformation, Embedded Software, Firmware, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Tensorflow, Sensor Fusion, Signal Processing, Software Engineering, Software Requirements Analysis, Feature Engineering, Pytorch, Model Validation, Reliability of Systems, Git, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Software Version Control, Data Pipelines - **Published:** May 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5b61a110081ea1f4 ## About the Role Do you have experience in Unit testing?, * 5+ years in machine learning engineering or applied ML, with a demonstrated track record of shipping models to production environments. * Programming: Strong proficiency in Python; hands-on experience with PyTorch or TensorFlow for model development and training. * Edge Deployment: Demonstrated experience optimizing and deploying models to edge or resource constrained targets using TFLite, ONNX, CoreML, TensorRT, or equivalent. * Data Engineering: Experience building and maintaining time-series or sensor data pipelines, including preprocessing, feature engineering, and data quality validation. * Model Optimization: Working knowledge of quantization, pruning, knowledge distillation, and other techniques for reducing model footprint and inference latency. * MLOps: Proficiency with experiment tracking tools (MLflow, Weights & Biases, or equivalent), model registries, and automated evaluation and testing workflows. * Software Engineering: Solid fundamentals - Git, code review, unit testing, and CI/CD - applied consistently to ML code, not just application code. * Cross-Domain Collaboration: 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. * Embedded Literacy: 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. Nice To Have: * 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., * Bachelor's degree in Computer Science, Electrical Engineering, Applied Mathematics, Data Science, or a related field required. * Advanced degree is a plus but not a substitute for hands-on experience shipping models to real systems ## Description We are looking for a Senior Machine Learning Engineer to own the AI/ML foundation of our physical AI initiative. This is not a role for someone who builds models in isolation and hands them off - you will be expected to own the full ML lifecycle, from raw sensor data to a model running on constrained hardware in the real world. You will work directly with embedded software, hardware, and systems engineers to bring AI capabilities into physical devices, and you will be accountable for the quality, reliability, and maintainability of every layer you touch. If you take pride in understanding how your model actually behaves on device, have strong opinions about data quality, and hold yourself to a high bar without being told to, you will thrive here., * 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., * Ownership: you own the behavior of the physical system end to end, from fieldbus packet to actuator response, and you do not hand problems off at the first sign of ambiguity. * Self-motivation: you identify gaps in integration coverage, tooling, and system reliability on your own, and you close them without waiting to be asked. * Problem-solving depth: you are not satisfied with a system that works most of the time; you understand the failure modes, quantify the risk, and drive to root cause. * Curiosity and continuous learning: the intersection of AI and physical systems is new territory, and you are drawn to it rather than cautious of it. * Direct, clear communication: you write well, translate hardware constraints into software requirements for ML collaborators, and surface timing and safety risks early. ## Related Videos - [From Perception to Autonomy: Building Agentic Edge AI Robots with ROS 2](https://www.wearedevelopers.com/videos/100295-from-perception-to-autonomy-building-agentic-edge-ai-robots-with-ros-2) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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