Software Engineer, Baseten
Model APIs
$150-230k
Offers Equity
ML application builder for data scientists
Be an early applicant
ML application builder for data scientists
21-100 employees
Be an early applicant
$150-230k
Offers Equity
21-100 employees
Company mission
To make machine learning accessible to every organisation.
Role
Who you are
- 3+ years experience building and operating distributed systems or large‑scale APIs
- Proven track record of owning low‑latency, reliable backend services (rate‑limiting, auth, quotas, metering, migrations)
- Infra instincts with performance sensibilities: profiling, tracing, capacity planning, and SLO management
- Comfortable debugging complex systems, from runtime internals to GPU execution traces
- Strong written communication; able to produce clear design docs and collaborate across functions
Desirable
- Experience with LLM runtimes (vLLM, SGLang, TensorRT‑LLM) or contributions to open-source inference engines (vLLM, TensorRT-LLM, SGLang, TGI)
- Knowledge of Kubernetes, service meshes, API gateways, or distributed scheduling
- Background in developer‑facing infrastructure or open‑source APIs
- We value infra‑leaning generalists who bring strong engineering fundamentals and curiosity. ML experience is a plus, but not required
What the job involves
- Baseten’s Model Performance (MP) team is responsible for ensuring the models running on our platform are fast, reliable, and cost‑efficient
- As part of this team, you’ll focus on Model API's — the infrastructure powering our hosted API endpoints for the latest open‑source models
- This work spans distributed systems, model serving, and developer experience. You’ll join a small, high‑impact team operating at the intersection of product, model performance, and infra, helping to define how developers interact with AI models at scale
- Design, build, and operate the Model APIs surface with focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling and multi-modal serving
- Profile and optimize TensorRT-LLM kernels, analyze CUDA kernel performance, implement custom CUDA operators, tune memory allocation patterns for maximum throughput and optimize communication patterns across multi-GPU setups
- Productionize performance improvements across runtimes with deep understanding of their internals: speculative decoding implementations, guided generation for structured outputs, custom scheduling and routing algorithms for high-performance serving
- Build comprehensive benchmarking frameworks that measure real-world performance across different model architectures, batch sizes, sequence lengths, and hardware configurations
- Productionize performance improvements across runtimes (e.g.TensorRT, TensorRT‑LLM): speculative decoding, quantization, batching, and KV‑cache reuse
- Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks to measure speed, reliability, and quality
- Implement platform fundamentals: API versioning, validation, usage metering, quotas, and authentication
- Collaborate closely with other teams to deliver robust, developer‑friendly model serving experiences
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 5 rounds)
Sep 2025
$150m
SERIES D
Feb 2025
$75m
SERIES C
Total funding: $285m
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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