Member of Technical Staff - Thermal Simulation Engineer

Vinci4D.ai; Palo Alto (CA); Hybride; USD 150 000 - 210 000; Plein temps

Résumé du poste

Vinci4D.ai is building an AI-driven thermal design platform. This hands-on role spans validation, data generation, and customer engagements, applying physics-based solvers to 2.5D and high‑density boards.

You will benchmark solvers against FEA tools, generate reference cases, run customer tests end to end, and translate results into actionable product requirements. The ideal candidate has deep thermal CFD/FEA experience, first‑principles reasoning, and strong communication with customers.

Qualifications

• BS or higher in mechanical, electrical, aerospace engineering, or a related technical field.
• 3+ years in thermal or simulation engineering; graduate research counts.
• Hands‑on experience with thermal/thermo‑mechanical CFD/FEA using listed tools.
• Practical meshing knowledge including convergence and quality metrics.
• Ability to reason from first principles and diagnose causes.
• CAD navigation and repair of real geometry in mechanical or ECAD.
• Strong communication skills for technical audiences.

Responsabilités

• Validate new solvers and methods by building test cases and benchmarking against FEA tools and data.
• Set accuracy bar for release and speak up when not met.
• Generate training data: reference cases, geometry sets, boundary conditions, and sweeps.
• Identify bugs and improve workflows on 2.5D packages and 12‑layer boards with root‑cause notes.
• Run customer cases end to end: ingest native geometry, produce results, interpret, and present.
• Support onboarding and training; answer technical questions.
• Build tooling: test harnesses, batch sweeps, regression comparisons, rapid prototyping with AI aids.
• Convert temperature and displacement fields into plots and design comparisons for leadership decks.
• Provide field observations to research and product with specific feature requests.

Connaissances

Thermal CFD/FEA

First principles reasoning

CAD fluency

Strong communication

Linux proficiency

Formation

BS in mechanical engineering

MS or PhD in a relevant field

Outils

Ansys Icepak

Flotherm

Fluent

Ansys Mechanical

COMSOL

OpenFOAM

Description du poste

About Us

We are building an AI assistant for hardware design. Our product helps engineers explore, analyze, and optimize hardware systems, integrating cutting‑edge AI with the realities of engineering design workflows.

Role Summary

Hands‑on technical role spanning validation, training data, and customers. Vinci’s physics foundation model produces solver‑accurate thermal results on full‑resolution hardware — real geometry, nanometer‑scale features, no simplification — across conduction, convection, and thermoelasticity. You’ll prove that case by case: validating new solvers and methods against competitor FEA tools and measured data, generating the reference cases that train the model, running customer test cases end to end, and bringing what you learn back to research and product as feature definition. The ideal candidate has a strong thermal simulation foundation, reasons from first principles, and is comfortable presenting a result to a customer’s thermal engineers who will not take your word for it.

What You’ll Do

• Validate new solvers and methods: build test cases that stress steady‑state and transient conduction, convection, and thermoelastic stress and warpage, and benchmark them against competitor FEA tools and measured data.
• Help set the accuracy bar for release, and say so when it isn’t met.
• Generate training data: build and run the reference cases that feed model training — geometry sets, material stacks, boundary conditions, parameter sweeps — and curate the results the model learns from.
• Serve as the product’s heaviest internal user: find the bugs, bad defaults, and workflows that break on a real 2.5D package or a 12‑layer board, and file them with a root cause attached rather than a screenshot.
• Run customer cases end to end: take a customer’s native geometry and power maps, produce a result, interpret it, and present it.
• Support customer onboarding and training, and own the technical questions that follow.
• Build your own tooling: test harnesses, batch sweeps, regression comparisons, and post‑processing, prototyping quickly with AI coding assistants.
• Turn temperature and displacement fields into plots, side‑by‑side design comparisons, and decks that hold up in front of a customer’s engineering leadership.
• Bring field observations back to research and product as specific requests: what to build, what the output needs to show, which physics matters to which customer.

Required Qualifications

• BS or higher in mechanical engineering, electrical engineering, aerospace engineering, or a comparably rigorous technical field.
• 3+ years in thermal or simulation engineering; graduate research counts.
• Hands‑on thermal or thermo‑mechanical CFD/FEA in Ansys Icepak, Flotherm, Fluent, Ansys Mechanical, COMSOL, or equivalent, including a working sense of where each tool’s results degrade.
• Practical meshing knowledge: mesh strategy, element selection, where and how to refine (interfaces, thin layers, boundary layers, high‑gradient regions), and how to qualify a mesh through independence studies, convergence behavior, and quality metrics such as aspect ratio and skewness. Our meshing is automated; evaluating what it produces takes the same judgment.
• Ability to reason from first principles: when a result looks wrong, to determine whether the cause is the physics, the boundary conditions, the geometry, or the software.
• CAD fluency: opening, navigating, interrogating, and repairing real geometry in mechanical CAD, ECAD, or package layout.
• Strong communication skills, including the ability to explain a result and its limits to a principal thermal engineer and to a VP in the same meeting, and to build the plots and slides yourself.

Preferred Qualifications

• Coding experience. Python is what we use, though any real scripting background counts (MATLAB, LabVIEW, C++, OpenFOAM customization). What matters is that you write your own scripts, internal tools, and one‑off analysis, including with AI coding assistants.
• 10+ years of thermal engineering, with shipped hardware behind it.
• Semiconductor packaging or high power‑density electronics: chip–package–board thermal, 2.5D/3D stacks, chiplets, power maps, TIMs, warpage and reliability, cold plates, vapor chambers, liquid cooling.
• Customer‑facing experience in applications, solutions, or field engineering, including demos, trainings, and presentations you delivered yourself.
• Experience validating or building a solver rather than only running one.
• Comfort working in Linux: command line, GPU/HPC job submission, version control.

Why Join

• You’ll work on semiconductor and advanced packaging hardware first — AI accelerators, chiplet assemblies, high‑power boards — then robotics, electric vehicles, power electronics, aerospace, and defense as those customers come on.
• You’ll work directly with the researchers and GPU kernel engineers building the model, not through a support tier.
• Customers use these results for production sign‑off, so validation calls carry real consequence.

Location

We’re in Palo Alto, CA near the Caltrain station, working 2–3 days a week in person. We hire remote for the right person — members of the team already work this way. Travel is occasional.

Reports to the Founding Mechanical Engineer, who reports to the CTO.

Compensation Range: $150K - $210K