Senior ML Engineer: Edge AI for Physical Devices

Goddard; Wilmington (MA); Presencial; USD 140.000 - 165.000; Tempo integral

Benefícios oferecidos por esta vaga

Flexible time off

401(k) retirement savings with company

Health insurance

Parental leave

Resumo da vaga

Goddard is seeking a Senior Machine Learning Engineer to own the AI/ML foundation of our physical AI initiative. You will manage the full ML lifecycle from raw sensor data to a model running on constrained hardware, working with embedded software, hardware, and systems engineers to deliver reliable AI capabilities on devices.

Applicants should have 5+ years in ML engineering, strong Python skills, and hands-on experience with PyTorch or TensorFlow.

Qualificações

• 5+ years in machine learning engineering or applied ML with shipping models to production.
• Proficient in Python; hands-on PyTorch or TensorFlow for model development and training.
• Experience deploying models to edge or constrained targets (TFLite, ONNX, CoreML, TensorRT).
• Experience building time-series or sensor data pipelines and data quality validation.

Responsabilidades

• Design and implement data pipelines for sensor data ingestion, preprocessing, labeling, and curation.
• Train, evaluate, and iterate on ML models for signal processing, anomaly detection, and physiological parameter estimation.
• Optimize models for edge deployment to meet latency and memory constraints.
• Deploy models to constrained hardware using runtimes and validate end-to-end inference on devices.
• Collaborate with embedded software to integrate ML inference into firmware and software stacks.
• Build and maintain MLOps infrastructure: experiment tracking, versioning, and CI/CD for models.

Conhecimentos

Python

PyTorch

TensorFlow

C/C++

Formação

Bachelor’s degree in Computer Science / Electrical Engineering / Applied Math / Data Science

Ferramentas

MLflow

Weights & Biases

ONNX

TensorRT

TFLite

CoreML

Descrição da vaga

Goddard is seeking a Senior Machine Learning Engineer to own the AI/ML foundation of our physical AI initiative. You will manage the full ML lifecycle from raw sensor data to a model running on constrained hardware, working with embedded software, hardware, and systems engineers to deliver reliable AI capabilities on devices.

Applicants should have 5+ years in ML engineering, strong Python skills, and hands-on experience with PyTorch or TensorFlow.