Hyliion is committed to creating innovative solutions that enable clean, flexible and affordable electricity production. The Company’s primary focus is to develop distributed power generators that can operate on various fuel sources to future-proof against an ever-changing energy economy.

Job Purpose

The Senior Manager, Additive Fleet is responsible for the day-to-day performance of Hyliion's laser powder bed fusion (LPBF) printer fleet in Austin, which produces the complex, high-density metal heat exchanger hardware in the KARNO Core. This hardware can only be produced through metal additive manufacturing, so Hyliion's ability to build KARNO at scale depends directly on the health, uptime, and throughput of the fleet. The role leads the technicians and operators who run and maintain the machines, and owns preventive maintenance strategy, machine health monitoring, and decisions on hardware and software upgrades. The fleet runs the full range of Colibrium Additive LPBF platforms, including new machine technology being adopted in real time, and a primary focus is reducing machine-to-machine variation and building repeatable processes across machine models. This is a hands-on leadership role with regular time on the shop floor, and its scope will grow as Hyliion's print capacity scales.

AI at Hyliion

At Hyliion, AI is core to how we work. We equip every team member with leading AI tools and count on you to use them — to move faster, solve harder problems, and help us realize the full potential of KARNO technology for the world.

Duties and Responsibilities

Own fleet uptime and drive continuous improvement in machine-to-machine consistency across Hyliion's LPBF printer fleet.
Build and maintain a preventive maintenance program across all machines to identify failure modes before they cause downtime.
Lead, schedule, and develop the team of technicians and operators who run and maintain the fleet.
Develop machine health monitoring using machine data and sensor feeds to predict failures, plan maintenance, and inform fleet-wide decisions.
Evaluate and make decisions on hardware and software upgrades as new capability becomes available from Colibrium Additive and other sources.
Lead fleet expansion planning as print capacity scales, including layout, staffing, and process readiness for new machines.
Build repeatable processes across different machine models and maintain high uptime as new platforms come online.
Own the learning curve on new machine technology and platforms, and build institutional knowledge as Hyliion adopts next-generation LPBF systems.
Walk the shop floor regularly to identify process improvement opportunities and drive changes that reduce variation and maximize uptime.
Troubleshoot and resolve issues that threaten fleet uptime, including outside of normal working hours when needed.
Additional duties and responsibilities as assigned, needed, or required for the business.

Qualifications

Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.

Qualifications include

Education, Experience and Certifications

Bachelor's degree in engineering or a related field, with a focus on advanced manufacturing.
5+ years of hands-on experience with LPBF (laser powder bed fusion) additive manufacturing in a production environment, rather than a lab or R&D setting.
Experience with Colibrium Additive (formerly GE Additive) machines strongly preferred.
Experience leading a team, either in a formal management role or as a lead technical contributor.

Skills and Abilities

Hands-on skill running and troubleshooting LPBF machines at the operator and technician level.
Track record of driving process improvement on production additive manufacturing equipment.
Thorough understanding of machine optical calibration and its impact on part quality and machine-to-machine consistency.
Ability to analyze machine data and sensor/log data to make fleet-level decisions on maintenance, upgrades, and process changes.
Understanding of the physics underlying LPBF machine operation, sufficient to predict machine performance and diagnose root causes of variation.