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Software Engineer - Model Performance Systems
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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 18 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…
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Baseten

Software Engineer - Model Performance Systems

Toronto, ON, Canada

$165,000 to $330,000 USD / year · Full-time

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Job details

Pay: $165,000 to $330,000 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 are looking for Software Engineers to join our team. This is a specialized, high-impact role sitting at the intersection of high-performance computing (HPC) and Large Language Model (LLM) engineering. You will not just be building the automated "speedometer and diagnostic" suite for our next-generation AI infrastructure; you will be defining the roadmap, driving key technical decisions, and taking full ownership of the future of this work.

Responsibilities

Benchmarking: Evaluate, run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse, disaggregated serving).
DevEx Improvement: Develop and maintain internal GPU-enabled development environments (similar to GitHub Codespaces). You will ensure the team has seamless, high-performance "dev machines" optimized for model experimentation.
Tool Development: Build and contribute to open-source tools such as InferenceMAX and genai-bench to automate model evaluation, benchmarking and analysis.
System Profiling: Use profilers like PyTorch Profiler, NVIDIA Nsight Systems and py-spy to collect performance profiles, identify bottlenecks, and debug the compute/networking stack.
Monitoring & Observability: Develop real-time dashboards and alerts to monitor system health, model startup times, and runtime performance.
Continuous Integration: Automate performance testing via CI/CD pipelines to catch regressions and build release workflow automation for the model runtimes stack.
Optimization Automation: Build tools to find the "Pareto frontier" - identifying the absolute best configuration (latency vs. cost vs. quality) for a given model and workload.

Requirements

This is a mid-senior, high leverage role. We care about your technical depth, strong communication skills to drive cross-team efforts, ability to navigate vague requirements and mentor other engineers. We want to talk to you if you have:

A Love for Systems & Hardware: You aren’t just interested in the AI; you want to understand GPU memory subsystems, InfiniBand, and how data moves across a cluster.
An Automation Mindset: You believe that if a task has to be done twice, it should be scripted. You have a passion for stress-testing and fuzzy testing to find the "breaking point" of a system.
Mathematical Curiosity: A desire to understand the underlying math of Transformers and how it translates into FLOPs and memory requirements.
Technical Toolkit: Familiarity with Python, and an eagerness to master the NVIDIA software stack. C++ familiarity is good to have.

WHY THIS ROLE

Direct Impact: Your tools will be the gatekeeper for what defines "good" performance for our customers.
Deep Learning (Literally): You will gain world-class expertise in GPU orchestration and LLM inference that few engineers in the industry possess.
High Ownership: As the lead of a small team, you will have the autonomy to build tools from scratch and contribute to open-source projects.

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: $165K - $330K

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More about this job

Responsibilities

Build and maintain benchmarking, profiling, monitoring, and automation tools to evaluate LLM quality and inference performance across models and workloads. Improve GPU-enabled development environments, automate performance testing and release workflows, and identify optimal configurations balancing latency, cost, and quality.

Requirements

This mid-to-senior role calls for strong systems and hardware knowledge, including GPU memory, cluster networking, and how Transformer computations translate into FLOPs and memory needs. Candidates should be familiar with Python, eager to learn the NVIDIA software stack, and able to lead cross-team work, navigate ambiguity, communicate effectively, and mentor engineers; C++ familiarity is beneficial.

Skills

Python
C++
High-Performance Computing
GPU Systems
LLM Inference
Benchmarking
Performance Profiling
System Optimization
Automation
CI/CD
Monitoring And Observability
Transformer Mathematics
Cross-Team Communication
Technical Leadership
Mentoring

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

Python
C++
High-Performance Computing
Large Language Models
LLM Inference
GPU
NVIDIA Software Stack
InfiniBand
GSM8K
MMLU
InferenceMAX
Genai-Bench
PyTorch Profiler
NVIDIA Nsight Systems
Py-Spy
Benchmarking
Model Evaluation
Performance Profiling
KV Cache Reuse
Disaggregated Serving
Long-Context Windows
GPU-Enabled Development Environments
GitHub Codespaces
Monitoring And Observability
CI/CD
Performance Regression Testing
Fuzzy Testing
Transformer Mathematics
Latency-Cost-Quality Optimization
Model Runtime

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