Senior Machine Learning Engineer - Physical AI

Our Mission: Through inspired engineering and design, we deliver outstanding solutions that positively impact lives. We use an interdisciplinary development process that combines our diverse engineering experience with creative industrial design solutions. We succeed when our partners succeed - it's all about solving the most complex challenges by creating transformative technology. Our Culture and People: At Goddard, our most important asset is our people. We don't just work together; we thrive together. We foster a culture of collaboration, continuous learning, and mutual support. We believe in taking exceptionally good care of each other because great teams build great solutions. If you are someone who embodies the values of accountability, inspiration, dedication, efficiency, innovation, integrity, quality, and reliability, we want you on our team. Come be a part of a workplace where your ideas are valued, your growth is encouraged, and your contributions make a real impact. Join us in shaping the future of transformative technology - together. The Role: 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. 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: 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. What We Value 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. Education Requirements: 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 Our Benefits: Flexible Time Off: Benefit from our generous flexible time off policy. We also provide sick leave and bereavement time because we understand that not all time off is for fun. Retirement Savings: Invest in your future with a 401(k)-retirement plan. Goddard contributes 3% of your annual salary directly into your 401(k) account-regardless of your own contributions. Health Coverage: Access to comprehensive medical, dental, and vision insurance for you and your family. Goddard contributes 80% of monthly premiums for all medical plan options. Family Support: To take the time you need to welcome the newest member of your family, Goddard offer 6 weeks fully paid parental leave with support of PFML state programs. Company Engagement: Engage with your colleagues through a variety of regular company and team events, including weekly social hours, Athletic Club outings, and department outings. The pay range for this role is: 140,000 - 165,000 USD per year (Wilmington Office) Goddard Inc.