From 240 unanswered applications toa Deep Learning Engineer offer at NVIDIA — in 47 days.
“I was applying to everything — systems, ML, backend — because I didn't know what to call myself. VitaeMaxx didn't write a better resume. They told me which engineer I was.”— MS CS candidate · repositioned as deep learning systems engineer
Background
BTech in Computer Science from IIT Madras, two years as a software engineer at TCS, then an MS in Computer Science at Carnegie Mellon with a Machine Learning specialization. Her capstone optimized CUDA kernels for sparse attention — a clean intersection of systems and ML she hadn't been telling anyone about.
Where she was stuck
240 applications. Three first-round phone screens. Zero offers. Five months into a 36-month STEM OPT window with no Plan B. Her resume tried to cover everything from distributed systems at TCS to ML research at CMU, so it read as a generalist in a market that hires for specialists.
Signal
Repositioned as a deep learning systems engineer. The CUDA capstone became the lead story. Resume, LinkedIn, and project README all rebuilt around accelerator-shaped work.
Align
Target Company Map prioritized accelerator and inference platforms: NVIDIA, AMD, Cerebras, Groq, Tenstorrent, Together, Fireworks, Anyscale, Lambda. 100+ leads filtered to 32 active ML-systems openings.
Win
For the NVIDIA Deep Learning Performance posting, the competitive analysis matched her capstone benchmarks to the team's recent paper. Network map surfaced a CMU alum on the adjacent team — warm intro in six days.
Before VitaeMaxx
- ✗240+ applications, three phone screens, zero offers
- ✗One generic resume covering systems and ML
- ✗No clear target list — applying to everything that said 'ML'
- ✗OPT clock at month 5 with no Plan B
After VitaeMaxx
- ✓32 well-aligned openings, 6 perfect-match
- ✓Resume rebuilt around the accelerator-systems narrative
- ✓Warm intro at NVIDIA via CMU alumni map
- ✓Onsite-ready interview plan and competitive analysis
H-1B cap sponsored