Software Engineer - GPU Kernels
- Toronto, ON, Canada
$180,000 to $360,000 USD / year · Full-time
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Job details
- Pay
- $180,000 to $360,000 USD / year
- Job type
- Full-time
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- Posting language
- English
- Posted
Original job description
About Baseten
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
THE ROLE
We’re seeking a GPU Kernel Engineer to join our team at the cutting edge of AI acceleration, where your code directly impacts the performance of state-of-the-art machine learning models. As a GPU Kernel Engineer, you'll craft the foundation that powers modern AI workloads, optimizing every microsecond of computation to enable breakthrough applications.
You'll work in a fast-paced, intellectually stimulating environment where technical excellence is paramount and your contributions directly influence production systems serving millions of users across numerous products. This role offers exceptional growth potential for engineers passionate about low-level optimization and high-impact systems work.
EXAMPLE INITIATIVES
You'll get to work on these types of projects as part of our Model Performance team:
Core Engineering Responsibilities
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Compensation Range: $180K - $360K
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
THE ROLE
We’re seeking a GPU Kernel Engineer to join our team at the cutting edge of AI acceleration, where your code directly impacts the performance of state-of-the-art machine learning models. As a GPU Kernel Engineer, you'll craft the foundation that powers modern AI workloads, optimizing every microsecond of computation to enable breakthrough applications.
You'll work in a fast-paced, intellectually stimulating environment where technical excellence is paramount and your contributions directly influence production systems serving millions of users across numerous products. This role offers exceptional growth potential for engineers passionate about low-level optimization and high-impact systems work.
EXAMPLE INITIATIVES
You'll get to work on these types of projects as part of our Model Performance team:
- Baseten Embeddings Inference: The fastest embeddings solution available
- The Baseten Inference Stack
- Driving model performance optimization
Core Engineering Responsibilities
- Design and implement high-performance GPU kernels for key ML operations, including matrix multiplications, attention mechanisms, and mixture-of-experts routing
- Write and optimize code using CUDA, PTX assembly, and architecture-specific techniques
- Apply advanced performance optimization methods such as memory coalescing, warp-level programming, tensor core acceleration, and compute/memory overlap
- Implement cutting-edge features like quantization (FP8/FP4), sparsity, and compute/communication overlap
- Identify and resolve performance bottlenecks using tools like Nsight Systems, Nsight Compute, and Torch Profiler
- Collaborate with research teams to productionize theoretical advancements
- Contribute to internal and open-source GPU libraries
- Present technical contributions at industry conferences (e.g., NVIDIA GTC, AWS re:Invent)
- Strong understanding of GPU architecture and programming paradigms:
- Memory hierarchy (global, shared, registers, L1/L2 cache)
- Thread/block/grid organization
- Synchronization techniques and race condition mitigation
- Proficient in C++ and GPU performance profiling tools
- Knowledge of:
- CUDA C++ API
- Memory access patterns and bandwidth optimization
- Numerical precision and quantization strategies
- Modern GPU features (e.g., tensor cores, async operations)
- Experience with Transformer models and attention optimization (e.g., Flash Attention)
- Familiarity with GPU kernel libraries: Cutlass, Triton, Thrust, CUB
- Background in GEMM tuning and distributed/multi-GPU compute
- Contributions to open-source GPU projects
- Research publications or conference presentations on GPU performance
- Competitive compensation, including meaningful equity
- (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
- Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
- Paid parental leave
- Fertility and family-building stipend through Carrot
- (U.S. only) Company-facilitated 401(k)
- Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Compensation Range: $180K - $360K
More about this job
Responsibilities
Design and optimize high-performance GPU kernels for machine-learning operations, using CUDA, PTX, and architecture-specific techniques. Identify and resolve performance bottlenecks, productionize research advances, and contribute to internal and open-source GPU libraries.
Requirements
Candidates should have a strong understanding of GPU architecture, memory hierarchies, thread organization, synchronization, and performance optimization, along with proficiency in C++ and GPU profiling tools. Knowledge of CUDA, memory access and bandwidth optimization, numerical precision and quantization, and modern GPU features such as tensor cores and asynchronous operations is required.
Skills
- GPU Kernel Development
- GPU Architecture
- CUDA C++
- C++
- PTX Assembly
- GPU Performance Profiling
- Memory Optimization
- Warp-Level Programming
- Tensor Cores
- Quantization
- Numerical Precision
- Attention Optimization
- GEMM Tuning
- Multi-GPU Computing
- Nsight Systems
- Nsight Compute
Benefits
- Equity
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Flexible Paid Time Off
- Company-Wide Winter Break
- Paid Parental Leave
- Fertility and Family-Building Stipend
- 401(k)
Visa sponsorship
Not detected in the job text
Categories
- Software
- Technology
- Engineering
- Data & Analytics
- Science & Research
Keywords
- GPU Kernels
- CUDA
- PTX Assembly
- C++
- Matrix Multiplication
- Attention Mechanisms
- Mixture-of-Experts
- Memory Coalescing
- Warp-Level Programming
- Tensor Cores
- FP8
- FP4
- Quantization
- Sparsity
- Compute-Communication Overlap
- Nsight Systems
- Nsight Compute
- Torch Profiler
- Transformer Models
- Flash Attention
- Cutlass
- Triton
- Thrust
- CUB
- GEMM
- Distributed Computing
- Multi-GPU Computing
- GPU Architecture
- Memory Hierarchy
- Inference Optimization