Job Description
Born out of MIT, VulcanForms is commercializing the first fully scalable, industrial metal additive manufacturing (AM) solution. Building the worlds most powerful and advanced metal AM system is the first step towards our vision of lights-out, highest quality metal AM mass production. We are a team of leading technology and operational experts with decades of experience in advanced manufacturing, materials, automation, and robotics. We are continuously seeking contributors who demonstrate outstanding integrity, intelligence, hunger, accountability, and a passion for learning.
VulcanForms is currently seeking a Machine Learning and Data Science Team Leader to join our team in Burlington, MA. In this role, you will be responsible for enabling data-driven intelligence for our state-of-art additive manufacturing (AM) processes. You will lead our growing team of Data, Machine Learning and Computer Vision engineers and scientists.
Your main objective will be to define and execute the Machine Learning and Perception strategies to enable our lights-out AM production including machine reliability of our industrial AM printers, manufacturing process stability and robustness, and the post-production quality inspection processes. The overarching goal is continuous, data driven improvement of the process reliability and production quality to provide our customers with superior parts that meet qualification criteria.
We are looking for someone with a visionary mindset, strong leadership skills and strong hands-on programming skills. You will have opportunity to shape the vision and the future of the digital manufacturing and explore still uncharted territories.
Your responsibilities will include:
- Enabling the in-situ feedback in the Digital Thread of our unique in-house LPBF process and taking it to the multi-factory scale
- Generating and organizing data feeding into the VulcanForms Digital Thread, to ensure process traceability and repeatability, and to create meaningful layers of the Digital Twin
- Automation and industrialization of the AM process, by designing and implementing in-situ sensing and vision-based inspection tools
- Exploring, identifying, and implementing the software tools and machine learning algorithms for process control and inspection
- Applying analytical and modeling approaches in developing data analytical tools for our processes.
- Mentoring your team members on advanced analytics tools, deep learning methods and apply analytical and modeling approaches in developing data analytical tools for our processes and provide company-wide technical leadership in the area of machine learning
REQUIREMENTS
- Masters degree in Computer Science, Data Science, Applied Mathematics, Robotics, or relevant Engineering discipline with a minimum of 8 years of professional relevant work experience or a PhD degree and a minimum of 6 years of relevant work experience
- 6+ years of experience in developing Machine Learning solutions and software for real-world applications across multiple domains
- Prior experience leading industrial projects involving development and implementation of machine learning-powered applications
- Experience in efficient handling, organizing and analyzing of large datasets
- Expert knowledge in data processing and statistical analysis
- Strong knowledge of at least one standard machine learning framework such as TensorFlow or PyTorch
- Must be a current US Citizen or US Resident/Green Card Holder
Preferred Additional Skills:
- Experience developing machine vision and machine perception systems
- Applied experience working on deep learning projects or developing deep learning algorithms
- Excellent software development skills in C++ and/or Python
- Self-motivated, capable of setting and reaching ambitious goals
- Demonstrated attention to detail and a strong desire to take work from concept to completion
- Ability to find simple and elegant solutions to hard technical problems
- Metal additive manufacturing industry experience
- Experience working in a fast-paced startup company environment