About the role
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).
Aplyr's read
Baseten simplifies machine learning deployment for engineers and data scientists, attracting talent focused on model performance and innovative tech solutions.
What's promising
- •Baseten offers a streamlined platform for deploying machine learning models, enhancing efficiency for data scientists.
- •The company hires specialized roles, indicating a focus on expertise in AI and machine learning.
- •Baseten's recent hiring in strategic finance and GTM roles suggests robust business growth and expansion.
What to watch
- •Limited public information about Baseten's financial stability and long-term viability.
- •The niche focus on machine learning may limit opportunities for broader tech roles.
- •Potential candidates may face competition due to the specialized nature of the roles.
Why Baseten
- •Baseten's platform specifically targets ease of model deployment, setting it apart from general tech firms.
- •The company's emphasis on post-training roles highlights a commitment to continuous model improvement.
- •Baseten's diverse engineering roles suggest a comprehensive approach to machine learning infrastructure.
Aplyr’s read is generated by AI from public sources. Was it useful?
About Baseten
Baseten is a platform that enables data scientists and machine learning engineers to deploy and manage machine learning models easily and efficiently.
