ML Infrastructure Engineer - New Grad
SambaNova Systems
$131,200 - $196,800 / year
Need a reasonable accommodation to apply or interview? Contact us.
JobMinglr uses automated technology to recommend jobs based on profile information and job preferences. Match Score does not determine eligibility for a position, prevent a user from viewing or applying to a job, or make hiring decisions on behalf of an employer.
Description
SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for customers worldwide. At the core of SambaNova's technology is the RDU (Reconfigurable Dataflow Unit) — a chip built on a dataflow architecture rather than the traditional GPU model. Its decode performance is especially strong for agentic workloads like multi-turn agents, code generation, and long-running applications. RDUs are packaged into SambaRack, rack-scale hardware that lets customers deploy state-of-the-art models with better performance, greater energy efficiency, and faster time to value.
About the Team
The ML Infrastructure team builds and operates the inference stack that serves SambaNova's models on our RDU accelerators. We cover request scheduling, caching, decoding techniques, public APIs, and the accuracy testing that every inference feature must pass before it ships.
About the Role
As a new grad ML Infrastructure Engineer, you'll write production Python for our inference serving stack, including our vLLM integration and public APIs. You'll help bring features like speculative decoding, prompt caching, and tool calling into production on the RDU, and add accuracy and regression tests that catch problems before customers do.
Responsibilities
- Implement inference features on the RDU such as speculative decoding, constrained decoding, tool calling, prompt caching, and long-context inference
- Adapt vLLM and related serving frameworks to the RDU architecture
- Build and maintain endpoints in the public inference API
- Write accuracy verification and regression tests for inference features
- Debug latency, throughput, and reliability issues in production services
Required Qualifications
- B.S. in Computer Science, Electrical Engineering, or related field
- Strong programming skills in Python
- Understanding of algorithms, data structures, concurrency, and systems design
- Coursework or project experience in distributed systems or machine learning
- Experience with LLM serving frameworks such as vLLM, SGLang, or TensorRT-LLM
- Experience with inference techniques such as speculative decoding, KV caching, or continuous batching
- Experience with PyTorch
Preferred Qualifications
- M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field
- Experience building or maintaining APIs or backend services
- Internship or research experience in ML systems or backend infrastructure
- Experience with disaggregated systems - prefill/decode
- Open source project ownership or contributions
Base Salary Range:
Submission Guidelines
Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.
EEO Policy
SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Benefits Summary for US-Based, Full-Time Employment Positions
SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.
Benefits
- health insurance
- stock options
About this role
SambaNova's ML Infrastructure team is looking for a new graduate to help build the inference serving stack that powers their RDU accelerators. You'd write production Python code for features like speculative decoding, prompt caching, and tool calling, working directly with vLLM and SambaNova's public APIs. The role involves adapting serving frameworks to the RDU architecture, writing accuracy and regression tests, and debugging performance issues in production—work that sits at the intersection of systems engineering and machine learning.
To succeed here, you'll need solid fundamentals: a CS or EE degree, strong Python skills, and real understanding of algorithms, data structures, and systems design. The job explicitly values hands-on experience with LLM serving frameworks (vLLM, SGLang, TensorRT-LLM), inference techniques like KV caching and continuous batching, and PyTorch. If you've done coursework or projects in distributed systems or ML infrastructure, that background will serve you well. Advanced degrees, API/backend experience, or open source contributions are a plus but not required.
This is an in-office role in Austin with a base salary of $131,200–$196,800 and a full benefits package including strong medical coverage, wellness programs, and equity.
How this employer is doing
average
- H1B: Sponsors Visas
- Sponsors Visas
This role's local market on JobMinglr
Pay for this role
$131,200 to $196,800 per year
That is the employer's posted range, and where you land in it is usually decided in one conversation. Before that conversation, run the numbers: analyze an offer for this role.
Before you apply, worth reading
How JobMinglr reads this job
Every listing here is scored against your profile before you apply: skills overlap, experience level, location and work arrangement, each weighted and explained. You see the score and the reasons, not just a list. How the matching works.