About this role
You will take on the following responsibilities:
- Analyze existing CPU-based modeling workflows to identify high-impact GPU acceleration opportunities
- Re-engineer modeling tooling and pipelines to exploit GPU parallelism
- Partner with modelers to validate that GPU-accelerated workflows maintain correctness and improve throughput
- Inform GPU infrastructure requirements based on actual workload characteristics
- Bridge between modeling teams and GPU specialists, translating domain requirements into technical specifications
You should possess the following qualifications:
- BS or MS in Science, Technology, Engineering or Math
- Minimum 1 year of experience required; 2-10 years of experience preferred
- Deep experience with quantitative modeling platforms and tooling — you've built or maintained the systems that modelers use daily
- Understanding of the full modeling lifecycle: feature engineering, simulation, model training, validation, and production deployment
- Familiarity with Python scientific computing ecosystem (NumPy, Pandas, scikit-learn)
- Strong relationship-building skills — you'll spend significant time working directly with modelers
Preferred qualifications:
- Experience profiling and optimizing computational workloads
- Familiarity with NVIDIA GPU-accelerated scientific computing ecosystem (CuPy, RAPIDS, cuDF)
- Background in distributed computing or HPC