AI Research Engineer – Applied Scientist, Compilers

NVIDIA

AI Research Engineer at NVIDIA focusing on compilers and low-level optimization. Developing AI compiler solutions and machine learning technologies with a focus on innovation and performance.

Posted 4/22/2026full-timeRemote • 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

Competitive salaries
Generous benefits package
Equity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonC++AI/ML systemsreinforcement learningTransformersPEFTLoRAdistributed trainingprompt engineeringmodel fine-tuning

Soft Skills

collaborationproblem-solvingcommunicationiterationexperimentation

Certifications

M.S. degreePhD degree