Senior Additive Manufacturing Process Engineer

Responsibilities

• Drive novel improvements across the L-PBF Additive Manufacturing (AM) process
• Act as a subject matter expert in Laser Powder Bed Fusion to improve material capability and cost
• Characterize, control, and qualify improvements to in-house designed L-PBF equipment
• Develop novel print file toolpath generation techniques and process parameters
• Utilize and improve material qualification frameworks to characterize AM material properties
• Collaborate with design, manufacturing, and quality teams on feasibility and risk assessments
• Author and maintain engineering specifications for printed material consistency
• Support technical disposition of AM quality non-conformances and drive root cause corrective action

Requirements

• B.S. or M.S. in Mechanical Engineering, Material Science and Engineering, or related field
• 5+ years of professional hands-on fabrication, assembly, testing, or technical project management experience
• 5+ years developing L-PBF equipment, process parameters, or print file programming
• Ability to lawfully access information subject to US export controls (ITAR)
• Ability to handle AM metal powders with proper PPE
• Ability to carry up to 25 lbs

Preferred Qualifications

• Proficiency with materials engineering fundamentals (Stress-Strain, Fatigue, Microstructure)
• Experience with Aerospace Design for AM and manufacturing trade studies
• Direct hands-on experience operating and programming Metal Additive Manufacturing Equipment
• Experience with root cause troubleshooting and repairing mechatronic systems
• Proficiency with 3D Modeling in Teamcenter / NX
• Experience utilizing and editing code for data analysis (Python and/or C++)
• Success with process parameter optimization through design of experiments and statistical analysis

About the Company

Divergent is a technology company that has architected, invented, built, and commercialized an end-to-end factory system called the Divergent Adaptive Production System (DAPS) that comprehensively uses machine learning to optimally engineer, additively manufacture, and flexibly assemble complex integrated vehicle structures and subsystems.