Process Design Engineer

Position Overview:

Precision Additive is a fast-growing startup specializing in laser powder bed fusion (LPBF) technology, with a mission to deliver production-ready additive solutions via process design, materials engineering, and rigorous quality systems.

As a Process Design Engineer, you will drive the development and optimization of subsystems and process signatures across our LPBF platforms. You will translate thermal–fluid dynamics into robust, scalable manufacturing processes, collaborating across the organization to accelerate product quality and performance. This role blends hands-on experimentation and computer-based modeling and analysis. You will also be responsible for designing and setting up build files using Materialise software and performing machine calibration activities to ensure optimal system performance and process consistency.

Key Responsibilities

Process Characterization & Signatures

• Design and execute experiments to map thermal, optical, and fluidic signatures of LPBF subsystems (e.g., powder delivery, melt pool dynamics, gas flow, scanner timing, laser intensity profiles)
• Develop and integrate sensor setups for real-time signature capture and feedback control

CFD & Simulation Modeling

• Develop and validate CFD models for melt-pool dynamics in Flow3D.
• Perform parametric studies and sensitivity analyses to guide process design.
• Assist in CFD analysis of Precision Additive machine design and optimization.

Build File Design and Setup

• Design, configure, and optimize build files using Materialise software, incorporating support strategies, part orientation, and parameter settings.
• Interface with machine controls and print preparation systems to ensure smooth data flow and execution.

Machine Calibration and System Readiness

• Perform and Document LPBF machine calibration procedures (e.g. laser focus, scanner alignment, gas flow tuning).
• Maintain calibration logs and work with vendors or internally to troubleshoot hardware and software issues.

Material Testing Coordination

• Plan and manage experimental campaigns with commercial testing laboratories and university research labs for powder characterization, CT scanning, microstructure evaluation, and mechanical testing.
• Coordinate sample preparation, test method selection, and data integration to support process design decisions.

Data Analysis & Visualization

• Build data pipelines and dashboards in Python (or similar) for visualization of process variables, test results, and simulation outputs.
• Use statistical tools (DOE, regression analysis) to identify key process drivers and optimize parameters.

Process Optimization & Scale-Up

• Apply root-cause analysis and DOE methodologies to improve part quality, yield, and reproducibility
• Support technology transfer activities, guiding scale-up from R&D to pilot and production systems

Cross-Functional Collaboration

• Work closely with R&D, machine design, and quality systems to embed process insights into product requirements
• Engage with universities and subcontractors to leverage external expertise and resources throughout process design and testing

Documentation & Quality Support

• Draft process design reports, technical presentations, and quality protocols (e.g., AS9100, ASTM standards)
• Contribute to internal quality frameworks (PAQ) and support customer audits or DoD submissions

Required Qualifications

• Bachelor’s or Master’s degree in Engineering, Materials Science, or related field
• Experience with LPBF systems and metallurgy
• 3 years of experience in process development, or additive manufacturing
• Proficiency in Materialise software
• Experience with machine–process integration, sensor data acquisition, or control system design
• Proficiency with CFD software (ANSYS Fluent, STAR-CCM , or similar) and data analysis tools (Python, MATLAB)
• Familiarity with design of experiments (DOE) and statistical analysis (Minitab, JMP)
• Knowledge of quality management standards (AS9100, ISO 9001) and regulatory requirements (ITAR, DoD procurement)
• Experience or familiarity in coding languages for data visualization and analysis
• Demonstrated ability to design experiments, analyze complex datasets, and distill actionable insights
• Strong time management skills, with ability to coordinate multi-site or cross-disciplinary campaigns
• Excellent written and verbal communication skills for technical and non-technical audiences