Pay
Higher than most similar jobs
$150K–$350K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
ML Systems, LLM Post-Training
San Jose · San Jose, California, United States
Actively hiring, first seen by us 1 day ago- Pay
$150,000–350,000/year — pay source
What we offer $150K/yr – $350K/yr + Offers Equity. We are open to discuss above-scale compensation with exceptional candidates on a case-by-case basis. Unlimited PTO and full benefits (medical, vision, dental, 401k).
Read the full posting- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Research and Machine Learning — team source
Department: Research and Machine Learning
From the employer’s posting
What you’ll work on
Full postingDesign, deploy, and optimize LLM inference systems across multi-node clusters
Implement and benchmark concrete inference optimizations
Profile inference bottlenecks from GPU kernel execution to memory bandwidth
Build evaluation harnesses measuring accuracy, throughput, latency, and utilization
Integrate new model architectures into production inference with research scientists
From the employer’s posting
Design, deploy, and optimize LLM inference systems across multi-node clusters
Implement and benchmark concrete inference optimizations.
Profile and analyze inference bottlenecks at the systems level—from GPU kernel execution to memory bandwidth constraints.
Build robust evaluation harnesses and benchmarking frameworks that measure accuracy, throughput, latency, and resource utilization across various parallelism strategies.
Collaborate with research scientists to integrate new model architectures and optimizations into production inference infrastructure.
What you’ll bring
All qualificationsCore experience
- Experience with large-scale ML systems, GPU computing, or high-performance inference optimization.
- Strong proficiency in Python and C++/CUDA; hands-on experience with SGLang, vLLM, PyTorch, or similar inference frameworks.
- Deep understanding of GPU architecture, memory hierarchies, and parallel computing paradigms.
- Experience deploying and optimizing LLMs in production: model serving, batching strategies, distributed inference, or quantization.
Preferred experience
- Familiarity with distributed computing frameworks (Ray, multi-node training/inference) is a plus.
Qualification wording
Experience with large-scale ML systems, GPU computing, or high-performance inference optimization.
Strong proficiency in Python and C++/CUDA; hands-on experience with SGLang, vLLM, PyTorch, or similar inference frameworks.
Deep understanding of GPU architecture, memory hierarchies, and parallel computing paradigms.
Experience deploying and optimizing LLMs in production: model serving, batching strategies, distributed inference, or quantization.
Familiarity with distributed computing frameworks (Ray, multi-node training/inference) is a plus.
Education & alternatives
Qualifications - B.S., M.S., or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent experience). - Experience with large-scale ML systems, GPU computing, or high-performance inference optimization.
Benefits in the posting
Full benefits wording- Unlimited PTO and full benefits (medical, vision, dental, 401k).
From the employer’s posting.
Posting record
Report an errorActively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 4, 20261 day ago
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 1
Early. Similar jobs usually come down between day 6 and day 70.
From 4,504 similar postings that came down. Coming down is not always a hire.
ChipAgents is hiring
3 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- C++
- Python
- PyTorch
Where the posting names them
- Experience with large-scale ML systems, GPU computing, or high-performance inference optimization. - Strong proficiency in Python and C++/CUDA; hands-on experience with SGLang, vLLM, PyTorch, or similar inference frameworks. - Deep understanding of GPU architecture, memory hierarchies, and parallel computing paradigms.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
Position Overview
We are seeking an ML Systems Engineer to optimize the performance and efficiency of large language model inference powering our agentic AI platform. This is a technical role focused on low-level systems optimization. You will implement performance optimizations, build evaluation harnesses, and architect multi-node clusters for training and inference that push the limits of LLM throughput and latency. Your work will directly impact the responsiveness and cost-efficiency of AI agents used by leading semiconductor companies to design chips.
Key Responsibilities
Design, deploy, and optimize LLM inference systems across multi-node clusters, maximizing throughput and minimizing latency for production workloads.
Implement and benchmark concrete inference optimizations.
Profile and analyze inference bottlenecks at the systems level—from GPU kernel execution to memory bandwidth constraints.
Build robust evaluation harnesses and benchmarking frameworks that measure accuracy, throughput, latency, and resource utilization across various parallelism strategies.
Collaborate with research scientists to integrate new model architectures and optimizations into production inference infrastructure.
Investigate and apply emerging techniques from research papers and open-source projects to continuously improve inference performance.
Qualifications
B.S., M.S., or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent experience).
Experience with large-scale ML systems, GPU computing, or high-performance inference optimization.
Strong proficiency in Python and C++/CUDA; hands-on experience with SGLang, vLLM, PyTorch, or similar inference frameworks.
Deep understanding of GPU architecture, memory hierarchies, and parallel computing paradigms.
Experience deploying and optimizing LLMs in production: model serving, batching strategies, distributed inference, or quantization.
Strong systems-level debugging and profiling skills; comfort working at multiple layers of the stack from CUDA kernels to application logic.
Familiarity with distributed computing frameworks (Ray, multi-node training/inference) is a plus.
Self-directed problem solver who is interested in working on ambitious optimization challenges.
Why Join Us
Work on cutting-edge LLM inference optimization problems with real-world production impact.
Access to substantial GPU compute resources for experimentation and benchmarking.
Collaborate with a world-class team spanning AI research, systems engineering, and EDA.
Shape the performance characteristics of AI systems used by leading semiconductor companies.
What we offer
$150K/yr – $350K/yr + Offers Equity. We are open to discuss above-scale compensation with exceptional candidates on a case-by-case basis.
Unlimited PTO and full benefits (medical, vision, dental, 401k).
Two engineering-centric offices with free parking, private gym, and free lunch, drinks and snacks.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
What we offer $150K/yr – $350K/yr + Offers Equity. We are open to discuss above-scale compensation with exceptional candidates on a case-by-case basis. Unlimited PTO and full benefits (medical, vision, dental, 401k).
- Location & working pattern
San Jose, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.