At Baseten we provide all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.
Get started in minutes, and avoid getting tangled in complex…
At Baseten we provide all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.
Get started in minutes, and avoid getting tangled in complex…
Notable recent funding: $300M Series E (company disclosure)
Company Overview
Problem Domain
Production inference and serving for machine-learning models (including LLMs) with emphasis on scalability, performance, and cost control.
Founded
2019
Industry
Software Development
Funding Track Record
Series B- March 2024
$40M
Series C- February 2025
$75M
Series D- September 2025
$150M
Series E
$300M
Company disclosure reporting $300M Series E at $5B valuation
Investor Signal
“Baseten has raised late-stage rounds with participation from investors including Bond, IVP, CapitalG, Spark Capital, NVIDIA, Greylock, Conviction, 01 Advisors, BoxGroup, and others.”
Founders
What we do
Join the Team
Software Engineer
On-SiteVancouver, CA
On-Site • Vancouver, CA
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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
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
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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
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
