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职位Baseten

Software Engineer, Model Performance Tooling

Baseten

Software Engineer, Model Performance Tooling

Baseten

Vancouver

·

On-site

·

Full-time

·

2mo ago

福利待遇

Parental Leave

Healthcare

必备技能

Node.js

TypeScript

PostgreSQL

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 $300M Series E, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.

THE OPPORTUNITY

We are looking for early-career Software Engineers to join our team in Vancouver, BC. This is a specialized role sitting at the intersection of high-performance computing (HPC) and Large Language Model (LLM) engineering. You will be responsible for building the automated "speedometer and diagnostic" suite for our next-generation AI infrastructure.

In this role, you won’t just be using models; you will be tearing them apart to see how they run on the metal. You will build tools that measure GPU FLOPS, stress-test Infini Band clusters, and define the benchmarks that ensure our systems are production-ready.

RESPONSIBILITIES

  • Performance Benchmarking: Run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse).

  • Infrastructure Validation: Create automated acceptance tests for new GPU clusters across x86 and ARM systems, measuring GPU memory bandwidth, networking throughput, and multi-node networking performance.

  • Model Dev Experience: 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 tools such as InferenceMAX and genai-bench to automate model evaluation and optimization.

  • Deep Hardware Profiling: Use Py Torch Profiler and NVIDIA Nsight Systems to collect performance profiles, identify bottlenecks, and debug the NVIDIA 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 in model setups before they hit production.

  • 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.

WHAT WE'RE LOOKING FOR

This is a fresher-friendly

role. We care more about your trajectory, curiosity, and technical depth than your years of experience. 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, Infini Band, 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.

  • Interest in Optimization: You are excited to learn about (or already play with) quantization, speculative decoding, disaggregated serving, and kernel-level optimizations.

  • 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 a small team of freshers led by experts, you will have the autonomy to build tools from scratch and contribute to open-source projects.

BENEFITS

  • Competitive compensation, including meaningful equity.

  • 100% coverage of medical, dental, and vision insurance for employee and dependents

  • Generous PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)

  • Paid parental leave

  • 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.

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关于Baseten

Baseten

Baseten

Series C

Baseten provides a platform for deploying and scaling machine learning models in production environments. The company offers infrastructure and tools for ML engineers to build, deploy, and monitor AI applications.

51-200

员工数

San Francisco

总部位置

$1.0B

企业估值

评价

4.1

10条评价

工作生活平衡

4.2

薪酬

2.8

企业文化

4.3

职业发展

3.5

管理层

3.2

72%

推荐给朋友

优点

Flexible work arrangements and schedules

Supportive team environment and good colleagues

Good benefits and health coverage

缺点

Below industry standard compensation and salary

Limited career advancement opportunities

High workload and stressful expectations

薪资范围

9个数据点

Junior/L3

L2

L3

L4

L5

L6

Recruiter

Junior/L3 · Recruiter

0份报告

$183,600

年薪总额

基本工资

-

股票

-

奖金

-

$156,060

$211,140

面试经验

52次面试

难度

3.3

/ 5

时长

14-28周

录用率

42%

体验

正面 66%

中性 21%

负面 13%

面试流程

1

Phone Screen

2

Technical Interview

3

Hiring Manager

4

Team Fit

常见问题

Technical skills

Past experience

Team collaboration

Problem solving