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Added 10 hours ago
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Added 12 hours ago
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Software Engineer - GPU Kernels
Baseten
Montreal, QC
- $180,000 to $360,000 USD / year
- Full-time
…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…
Added 12 hours ago
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Software Engineer - Model Performance Systems
Baseten
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- $165,000 to $330,000 USD / year
- Full-time
…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…
Added 12 hours ago
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Software Engineer - Voice AI (Inference Runtime)
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Added 12 hours ago
B
Software Engineer - Model Performance Systems
Baseten
Toronto, ON
- $165,000 to $330,000 USD / year
- Full-time
…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…
Added 12 hours ago
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Added 12 hours ago
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Added 12 hours ago
B
Software Engineer - GPU Kernels
Baseten
Toronto, ON
- $180,000 to $360,000 USD / year
- Full-time
…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…

Baseten

Software Engineer - GPU Kernels

Montreal, QC, 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
Apply by: Nov 28, 2026
Posting language: English
Posted: Oct 30, 2026

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:

Baseten Embeddings Inference: The fastest embeddings solution available
The Baseten Inference Stack
Driving model performance optimization

Responsibilities

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

Performance & Innovation

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

Impact & Collaboration

Contribute to internal and open-source GPU libraries
Present technical contributions at industry conferences (e.g., NVIDIA GTC, AWS re:Invent)

Requirements

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)

NICE TO HAVE

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

Benefits

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.

Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.

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

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More about this job

Responsibilities

Design and optimize high-performance GPU kernels for machine learning operations, using CUDA, PTX, and architecture-specific techniques to improve performance. Identify bottlenecks with profiling tools, implement features such as quantization and sparsity, and collaborate with research teams to bring advances into production and contribute to GPU libraries.

Requirements

Candidates should have a strong understanding of GPU architecture, memory hierarchy, thread organization, synchronization, and race-condition mitigation, along with proficiency in C++ and GPU profiling tools. They should know CUDA, memory access and bandwidth optimization, numerical precision and quantization, and modern GPU features; experience with transformers, kernel libraries, GEMM tuning, multi-GPU computing, or open-source contributions is a plus.

Skills

GPU Kernel Development
GPU Architecture
CUDA C++
PTX Assembly
C++
GPU Performance Profiling
Memory Hierarchy Optimization
Memory Coalescing
Warp-Level Programming
Tensor Core Acceleration
Quantization
Numerical Precision
Attention Optimization
GEMM Tuning
Distributed and Multi-GPU Computing
Performance Bottleneck Analysis

Benefits

Competitive Compensation
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)
Learning and Networking Opportunities

Visa sponsorship

Not detected in the job text

Keywords

GPU Kernels
CUDA
PTX Assembly
C++
GPU Architecture
Matrix Multiplication
Attention Mechanisms
Mixture-of-Experts Routing
Memory Coalescing
Warp-Level Programming
Tensor Cores
FP8
FP4
Quantization
Sparsity
Compute and Communication Overlap
Nsight Systems
Nsight Compute
Torch Profiler
Transformer Models
Flash Attention
Cutlass
Triton
Thrust
CUB
GEMM Tuning
Distributed Computing
Multi-GPU Computing
Machine Learning Inference
GPU Performance Optimization

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