Senior Machine Learning Engineer ml/python/Wilmington ma

Motion Recruitment Partners LLC; Wilmington (MA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical Insurance

Dental Benefits

Vision Benefits

Paid Time Off (PTO)

401(k) including match

Job summary

Motion Recruitment Partners LLC seeks a Senior Machine Learning Engineer to own the full ML lifecycle—from raw sensor data to a model running on constrained hardware.

You will design and utilize data pipelines for sensor data, train, optimize, and deploy models for real-time edge applications, and collaborate with embedded engineers to validate inference within device software. You will also build MLOps infrastructure and support regulatory submissions.

Qualifications

• Strong experience with Python and ML frameworks (PyTorch or TensorFlow).
• Ability to train, optimize, and deploy ML models for edge devices.
• Experience deploying models to edge using TFLite, ONNX, CoreML, or TensorRT.
• Experience building sensor data pipelines for ML workflows.
• Proficiency with MLOps and experiment-tracking tools.
• Solid software engineering fundamentals and proficiency in C/C++.

Responsibilities

• Own the full ML lifecycle for sensor data to edge deployment.
• Design and optimize data pipelines for real-time signal processing.
• Train, evaluate, and deploy ML models for edge devices and anomaly detection.
• Collaborate with firmware/embedded teams to validate inference in device software.
• Build and maintain MLOps infrastructure for experiments and deployments.
• Document model development for regulatory submissions and quality processes.

Skills

Python

PyTorch

TensorFlow

Edge deployment

MLOps

Software engineering

C/C++

Education

Degree in a relevant field

Tools

TFLite

ONNX

CoreML

TensorRT

Job description

Job Description

A full-service product development consultancy specializing in medical devices, robotic systems and automation is hiring a Senior Machine Learning Engineer. As a Machine Learning Engineer you will be expected to own the full ML lifecycle, from raw sensor data to a model running on constrained hardware.

In this role you will design and utilize data pipelines for sensor data. Your responsibilities include training, optimizing, and deploying machine learning models for signal processing and anomaly detection on edge devices, collaborating with embedded engineers to integrate and validate inference within device software. Additionally, you will build MLOps infrastructure, participate in sensor selection and validation, and document model development to support both regulatory submissions and internal quality processes.

Required Skills & Experience

• Strong proficiency in Python
• Hands on experience in PyTorch or TensorFlow
• Experience deploying models to edge using TFLite, ONNX, CoreML, TensorRT, or equivalent
• Experience building sensor data pipelines
• Proficiency with MLOps
• Solid Software engineering fundamentals
• Proficiency in C or C++

Desired Skills & Experience

• 5 years of machine learning engineering or applied ML
• Experience with physiological signal processing for medical or wearable applications
• Background in robotics, or autonomous systems
• Experience in a startup or small team
• Degree in a relevant field

What You Will Be Doing

Daily Responsibilities

• 100% Hands On
• Develop and troubleshoot workflows for collecting, cleaning, and organizing sensor data.
• Build and refine ML models for real-time device applications and performance improvements
• Work closely with firmware teams to embed and test AI features on hardware platforms
• Set up and oversee tools for tracking experiments, automating evaluations and managing deployments
• Analyze model behavior, ensure reliability and resolve issues to maintain highquality outputs

The Offer

• Bonus OR Commission eligible

You will receive the following benefits

• Medical Insurance
• Dental Benefits
• Vision Benefits
• Paid Time Off (PTO)
• 401(k) {including match -if applicable}

Applicants must be currently authorized to work in the US on a full-time basis now and in the future.