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Fluid Simulation Engineer

Palo Alto, CA, US
D

DeepSim, Inc.

Startup
Category
Technical
Experience
0-3 years
Type
Full-Time
Location
Palo Alto, CA, US
Salary
$140,000 - $180,000
Job Description
Benefits & Culture
Flexible hours
Remote work
Paid time off
Fast-paced environment
Direct feedback
Growth opportunities
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Response Rate
85%
Avg Response Time
2 hours
Compensation Calculator
In startups, higher salary usually means lower equity.
Salary (affects equity %)
Link salary ↔ equity
$125,000
$140,000$180,000
Equity % (auto from trade-off model)
1.19%
0%5%
Projected Exit Value
$350,000,000
$10M$500M
Live Trade-off
Salary
Equity %
Est. Equity Value at Exit
$100,000
1.19%
$2,082,500
$125,000
1.19%
$2,082,500
$150,000
1.02%
$1,785,000
$175,000
0.59%
$1,041,250

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Role Overview

We are looking for a Fluid Simulation Engineer with deep expertise in numerical simulation of fluid flow. You’ll play a key role in advancing DeepSim’s core simulation engine by designing solvers, modeling complex flow regimes, and enabling high-performance inference for large-scale engineering use cases.

Responsibilities

  1. Develop, implement, and optimize CFD/FEM-based fluid solvers for convection and heat transfer PDEs with multiphysics interactions.
  2. Investigate and enhance solver stability, accuracy, and performance across a variety of regimes and geometries.
  3. Build core simulation components and modeling pipelines that support AI-driven simulation frameworks.
  4. Collaborate with AI and systems engineers to create hybrid numerical/ML simulation architectures.
  5. Evaluate new numerical techniques, discretization strategies, and high-performance computing optimizations.

Qualifications

  1. Strong background in fluid simulations using CFD or FEM methods.
  2. Deep understanding of numerical solver mechanics and experience building CFD/FEM tools from scratch or modifying existing open-source frameworks.
  3. Proficiency in Python and C/C++, with experience writing high-performance and maintainable scientific code.
  4. Familiarity with HPC environments, GPU acceleration, or distributed computing is a plus.
  5. Master’s or PhD in engineering, applied physics, or a related discipline.