Advanced Engineer, Additive Process

Hyliion•Austin, TX

About The Position

Step into the world of metal additive manufacturing for the KARNO generator , where advanced laser powder-bed fusion meets real engineering challenge. Engineers in this group work across machine behavior, process optimization, material performance, data analytics, and hardware development to ensure high-quality, repeatable production using metal laser powder-bed fusion systems. If you’re the kind of engineer who enjoys digging into machine behavior, fine-tuning laser strategies, studying how materials evolve layer by layer, and pushing metal AM systems to run cleaner, faster, and more consistently, this team is built for you. The work is hands-on, data-driven, and central to scaling a first-of-its-kind distributed power technology. In this role, you’ll move across the entire additive manufacturing ecosystem. You’ll investigate how machine settings influence melt pools, porosity, and microstructure, and you’ll design controlled experiments to refine parameters and boost repeatability. A big part of the job involves troubleshooting machine quirks, interpreting sensor data, and making sense of real-time process feedback. You’ll also be working closely with design, materials, and hardware teams to bring complex AM components from early prototypes into reliable production. Along the way, you’ll help build the systems, standards, and workflows that turn metal AM into a high-trust, high-reliability manufacturing process for power-generation hardware.

Requirements

Bachelor’s degree in mechanical, materials science, aerospace, or related engineering discipline.
Requires a minimum of 5 years of engineering experience after earning a bachelor’s degree; relevant graduate project work can be applied toward requirements.
Direct, hands-on experience with laser powder bed fusion (LPBF) additive manufacturing REQUIRED .
Background in fabrication, prototyping, testing, or process development within an engineering environment.
Ability to interpret engineering drawings, apply GD&T, and understand material properties data.
Strong analytical and root-cause problem-solving skills.
Comfortable working hands-on with machines, tools, test equipment, and mechanical assemblies.

Nice To Haves

Build prep/simulation software like ANSYS Additive Suite, 3DXpert, or Dyndrite.
Experience developing AM parameters, designing DOEs, or using statistical analysis tools (SPC, JMP, Minitab, Python, MATLAB).
Experience troubleshooting AM machines, calibrating optics, repairing systems, or working with machine sensors and controls.
Knowledge of DFAM principles, heat-treat processes, and additive-appropriate material behaviors (e.g., nickel alloys, titanium).
Familiarity with CAD tools (NX, SolidWorks, or similar) and simulation/analysis workflows.
Experience with IQ/OQ/MQ qualification approaches.
Strong communication skills and ability to present findings to cross-functional teams.
Experience in a fast-paced R&D or startup hardware environment.

Responsibilities

Identify and optimize critical-to-function (CTF) parameters that drive mechanical performance, density, porosity, microstructure, and repeatability.
Design and execute experiments (DOE) to map the process window, understand sensitivities, and reduce variation.
Build and maintain parameter sets for new alloys, new hardware, and new machine configurations.
Work with sensor data, log files, and monitoring systems to correlate in-process signatures with part quality.
Develop statistical models, SPC controls, and trend analysis tools to ensure stable production behavior.
Improve AM machine capability through hardware tuning, sensor integration, calibration routines, and control-system optimization.
Lead machine troubleshooting, repair activities, and upgrades to improve uptime, repeatability, and print speeds.
Conduct machine characterization using advanced tools and metrology equipment to map laser optics, powder behavior, thermal gradients, and recoater performance.
Support machine-level IQ/OQ/MQ qualification and ongoing PM/maintenance strategies.
Collaborate with equipment vendors and internal engineering teams on new machine features, experimental setups, and pilot systems.
Drive automation, digital manufacturing, and data-acquisition improvements for machine performance monitoring.