AI Research Engineer – Applied Scientist, Compilers

NVIDIA

AI Research Engineer developing low-level optimization and compilers for NVIDIA’s AI infrastructure. Collaborating with engineers to enhance machine learning systems while applying advanced AI techniques.

Posted 5/8/2026full-timeSanta Clara • California, Texas, Washington • 🇺🇸 United StatesMid-LevelSenior💰 $152,000 - $241,500 per yearWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Help trailblaze company efforts in applying AI within conventional compilation pipelines.
Design and implement AI-based technology addressing core problems of low-level GPU programming.
Build training pipelines for supervised fine-tuning and reinforcement learning (RL/RLHF-style or policy optimization variants).
Define model inputs/outputs over compiler low level compiler representations.
Develop evaluation frameworks to measure code quality, runtime, compile-time overhead, and correctness.
Intelligent (domain task based) prompt engineering.
Collaborate with compiler engineers to integrate learned policies into production toolchains.
Prototype and iterate on model architectures, prompts, and fine-tuning strategies for scheduling and allocation tasks.
Create datasets from compiler traces, optimization passes, and target-specific performance signals.
Apply RL techniques to optimize for downstream objectives (performance, spill reduction, instruction-level parallelism, etc.) and run rigorous experiments, ablations, and benchmarking across workloads and hardware targets.

Requirements

What you’ll need

M.S./PhD degree in Computer Engineering, Computer Science related technical field (or equivalent experience).
5+ years of experience building AI/ML systems.
Strong software engineering skills in Python and at least one systems language (C++ preferred).
Hands-on experience training/fine-tuning large models (Transformers, PEFT/LoRA, distributed training).
Solid understanding of machine learning fundamentals and experimentation best practices.
Experience with reinforcement learning (e.g., policy gradients, actor-critic, offline RL, bandit-style optimization).
Knowledge of prompt-engineering techniques
Ability to work across research and engineering, from prototype to production.

Benefits

Comp & perks

equity
generous benefits package

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AIGPU programmingsupervised fine-tuningreinforcement learningmodel architectureprompt engineeringPythonC++Transformersdistributed training

Soft Skills

collaborationproblem-solvingcommunicationexperimentationiteration

Certifications

M.S. degreePhD degree