Senior Machine Learning Engineer – Physical AI

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.

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)