Senior Machine Learning Engineer – Physical AI

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, Tensor
  
  RT, 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 Tensor Flow for model development and training.
• Edge Deployment:
  Demonstrated experience optimizing and deploying models to edge or resource constrained targets using TFLite, ONNX, CoreML, Tensor
  
  RT, 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.
• MLOp…