Thermal and Fluid Systems Senior Engineer

We are looking for a talented Thermal and Fluid Systems Senior Engineer to join our team specializing in engineering for our Cummins’ business in Columbus, IN. 

In this role, you will make an impact in the following ways: 

Drive root-cause problem solving for thermal/fluid issues across components and systems—linking symptoms to causal mechanisms and owning closure with clear, traceable documentation. 

Elevate decision quality by executing credible modeling and test analyses using GT‑Suite/Boost, Fluent/Converge, Concerto, CDAT, Mach, DARTS, and Emissions data systems—triangulating simulation and empirical data for confident recommendations. 

Accelerate program delivery by rigorously applying PPP, PPT, VPI/VPC, ESW, and FEP—ensuring deliverables, gates, and risk mitigations are met with the right technical evidence at each milestone.

Improve product performance and durability through design-of-experiments, feasibility studies, and optimization (thermal management, pressure drop, emissions/after treatment, cavitation/aeration) that translate into measurable KPI gains. 

Strengthen calibration and controls integration by using Calterm, ECM tools, ACCEPTS, and in‑loop test benches to validate thermal/flow models against control strategies and real hardware behavior.

Clarify complex trade‑offs for stakeholders—synthesizing insights into concise briefs that inform architecture choices, supplier selections, and manufacturing feasibility in partnership with product/manufacturing engineering. 

Raise process capability by standardizing analysis templates, test data calculation methods, storage hygiene, and model correlation practices—reducing rework and enabling reuse across programs.

Enable team throughput by coordinating technician/student work scopes, mentoring peers on best‑practice workflows, and proactively communicating status/risks so projects progress without surprises.

To be successful in this role you will need the following: 

Strong technical depth in thermal, fluid, and energy conversion sciences—including the ability to model, simulate, test, and interpret system behavior using tools such as GT‑Suite, CFD, DOEs, statistical methods, and thermodynamic/heat‑transfer principles to drive accurate, data‑based decisions. 

Expertise in systems engineering and failure mode avoidance, enabling you to translate stakeholder needs into clear requirements, anticipate interactions and risks across components, and ensure reliability through structured FMEA, root‑cause analysis, and rigorous problem‑solving processes. 

Effective communication, collaboration, and decision‑making skills to work across engineering, manufacturing, suppliers, and technical leaders—delivering clear insights, negotiating trade‑offs, and keeping projects moving forward under ambiguity.

Proactive ownership of development, execution, and continuous improvement, including disciplined documentation, a commitment to self‑development, valuing diverse perspectives, and the ability to drive results through structured planning, controls understanding, and cross-functional alignment.

Education/Experience:

• College, university, or equivalent Bachelor's degree in Engineering or appropriate STEM field is required. Post-graduate (Master's) degree relevant to this discipline area may be required for select roles. Bachelor's or Master's in computer science and data science. 
• Python coding expertise
• Machine learning expertise
• Fundamental understanding of thermal and fluid based physics as they specifically apply to fuel systems
• This position may require licensing for compliance with export controls or sanctions regulations. Entry level/Early career professional. Preferred candidates would have relevant experience working in either a temporary student employment environment (intern, co-op, or other extracurricular team activities) or as an early career professional in a relevant technical discipline area. Knowledge of MS Office tools is also preferred.

Additional Details:

• Develop fuel systems specific reduced order models (ROMs) using advanced machine learning techniques for rapidly and accurately predicting injector performance behaviors such as needle lift, rate shape, body pressure ripple, etc.
• Create a well-documented, shareable GUI with guidance on use and limitations for ROM democratization
• Continue development of a fueling‑vs‑on‑time ROM
• Support rate‑shape synthesis by using ROMs to reverse‑engineer criti