Deep Learning Platform Engineer 1900S-5

E Ink Corporation
Billerica, MA

Job Description

Job Description

Team up with the most innovative company where Imagination becomes reality! E Ink is the originator, pioneer, and commercial leader in ePaper technology. The Billerica Research & Development team is thriving and growing as we help develop products that are changing how people access information every day. We are seeking qualified candidates who are self-driven, looking to advance their career and become a high-impact player on a team.

Based on technology from MIT’s Media Lab, E Ink has transformed and defined the eReader market. Its Electrophoretic Display products make it the worldwide leader for ePaper. The Company’s corporate philosophy aims to deliver revolutionary products, user experiences and environmental benefits through advanced technology development.

Our diversity of people, backgrounds, experiences, thoughts and perspectives is fostered to create an inclusive work environment. Our culture is built on value commitments to innovation, quality, results, integrity, community, people, and collaboration that fosters a strong employee engagement, teamwork, safety and wellness.

We offer a competitive/generous benefits package that fits the needs of our employees. It includes health, dental, vision, wellness programs, employee discounts, 401k matches, ongoing development, advancement opportunities and more. This position is also eligible for our bonus program. (see E Ink │ Our Company)

About the position:

E Ink provides the exciting and enabling “electronic paper” display technology used by eReaders throughout the world to provide a low-power, paper-like display. E Ink is seeking an experienced engineer to lead the design and deployment of our deep learning platform in a factory setting. You will architect and own the core infrastructure that trains, benchmarks, deploys, and monitors deep learning models with robustness and efficiency required at production scale. From data pipelines to multi-cluster orchestration to cycle time optimization, your work will be key to transforming R&D breakthroughs into scalable factory solutions. This role is highly collaborative — interfacing with R&D, IT, and manufacturing to ensure seamless deployment and iteration of our deep learning workflows.

Responsibilities:

  • Develop and maintain software to interface with the data collection apparatus
  • Develop and optimize scalable training pipelines across cloud and edge environments
  • Build and maintain CI/CD infrastructure for DL model training, testing, and deployment
  • Administer and scale on-prem and cloud GPU clusters
  • Automate data processing and real-time model evaluation at edge
  • Monitor, diagnose and debug data collection and model failures
  • Implement observability and performance metrics collection across the ML lifecycle
  • Collaborate with R&D, IT, and factory teams to productionize deep learning solutions

Qualifications:

  • Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field
  • 4+ years engineering experience with infrastructure/ML Ops
  • Proficient in Python, with experience using TensorFlow and/or PyTorch
  • Expertise in Docker, Kubernetes, and GPU-based orchestration at scale
  • Experience with hybrid cloud + on-prem or edge stack
  • Fluency in CI/CD best practices for ML pipelines, including model versioning and registry tools
  • Bonus: Experience with robotics or manufacturing-scale deployments

Benefits:

  • Competitive total compensation package
  • Medical, dental and vision on 1st day
  • Company 401K match
  • 20 PTO days
  • Generous sick leave policy
  • Casual day to day work environment
  • Hybrid/flexible work environment (for some positions)

E Ink is committed to a diverse and inclusive workforce. E Ink is an equal opportunity employer and does not discriminate on the basis of race, ethnicity, gender, identity, sexual orientation, veteran’s status, disability, age, or on any basis prohibited by federal and state law.

Posted 2025-07-24

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