Software Engineer, Baseten
Model Performance Tooling
CA$130-200k
+ Equity
ML application builder for data scientists
Be an early applicant
ML application builder for data scientists
21-100 employees
Be an early applicant
CA$130-200k
+ Equity
21-100 employees
Company mission
To make machine learning accessible to every organisation.
Role
Who you are
- 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, 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
- 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
What the job involves
- 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 InfiniBand clusters, and define the benchmarks that ensure our systems are production-ready
- Performance Benchmarking: You'll 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 PyTorch 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
Our take
As with its mathematical namesake, Baseten focuses on simplifying the complex. The San Francisco startup focuses on simplifying the integration of artificial intelligence into businesses. The tools that Baseten built demolish the need for businesses to hire specialised engineers, utilising the huge strides in artificial intelligence that we’ve seen in the last few years without adding workload to businesses.
With serious funding behind it, Baseten has grown its team by three times year-over-year over the last five years, especially across engineering and marketing. Having hired talent from companies such as GitHub, Google, Uber, Amazon, and Palantir, part of Baseten's focus is on helping companies implement the tools that they have at their disposal, a long-overlooked aspect of machine learning that many businesses have been eager to champion, but uncertain on how to actually use.
Simplifying the complex world of artificial intelligence for business is no mean feat but the three founders, who met whilst working as engineers at Gumroad, have a lot of experience in how to navigate this gap in the market.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 6 rounds)
Jan 2026
$300m
LATE VC
Sep 2025
$150m
SERIES D
Total funding: $585m
Company benefits
- Remote-first work environment. The Baseten team is welcome to work from wherever they want; fully remote, in our San Francisco office, or a mix of both. Today, our team (including our founding team) is spread across the United States, Canada, and Armenia. We provide a $1,000 stipend for you to make your home-office comfortable and productive
- Regular in-person team summits. We get together as a team three times a year to plan, workshop, and most importantly, get to know each other better
- Unlimited PTO. We ask that everyone take at least 4 weeks of vacation. And we have a company-wide break between Christmas and New Year's Day
- Full healthcare coverage. Medical, dental and vision insurance for you and your family
- Paid parental leave. 16-weeks fully paid parental leave (adoptive and non-birth parents included) and flexibility with schedules while returning to work
- Company-sponsored 401(k) for you to contribute to
- Learning and development budget. We encourage you to take classes, attend conferences, and invest in your craft and we’ll cover expenses to make it happen
Company HQ
Union Square, San Francisco, CA
Leadership
Tuhin Srivastava
(Co-Founder)Previously Co-Founder at Shape, Machine Learning at Gumroad and Skulpt.
Amir Haghighat
(Co-Founder)Previously Head of Engineering at Gumroad, Software Engineer at Yelp and Engineering Manager at Clover Health.
Phillip Howes
(Co-Founder)Previously Co-Founder at Shape, Machine Learning Engineer at Gumroad and Research Assistant at the University of Sydney.
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