This one is closed
Live roles like this one
-
C
19h ago
Staff Machine Learning Engineer
Uber Chicago, IL; New York, NY; San Francisco, CA; Seattle, WA; Sunnyvale, CA
- D 1d ago
-
D
1d ago
Jumio Anywhere in the World
-
D
1d ago
Machine Learning Engineer - IV (Biometrics)
Jumio Anywhere in the World
See every "Inference Optimization" role →
Get new “Inference Optimization” roles by email
One email a day with what is new in "Inference Optimization". Nothing new, no email.
We confirm the address first, and every mail carries an unsubscribe link. Alerts are ours, not a third party's.
Why this grade This listing scored 18/100, which is an F. It lost the most ground on pay transparency. See the breakdown
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Corroboration 5 / 10 Whether more than one source carries this listing.
- Role specificity 0 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Freshness 0 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Pay transparency 0 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
-15 Ghost-job penalty — Deducted for signals that this posting may not be a real, currently-open role — staleness, repeated relisting, or talent-pool language.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
Role Overview
We are seeking an AI Researcher with deep experience in inference optimization to design, evaluate, and deploy high-performance inference systems for large-scale machine learning models. You will work at the intersection of model architecture, systems engineering, and hardware-aware optimization, improving latency, throughput, and cost efficiency across real-world production environments.
Key Responsibilities
Research and develop techniques to optimize inference performance for large neural networks.
Improve latency, throughput, memory efficiency, and cost per inference.
Design and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications).
Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization).
Benchmark inference workloads across hardware accelerators.
Collaborate with engineering teams to deploy optimized inference pipelines.
Translate research insights into production-ready improvements.
Required Qualifications
Strong background in machine learning, deep learning, or AI systems.
Hands-on experience optimizing inference for large-scale models.
Proficiency in Python and modern ML frameworks (e.g., PyTorch).
Experience with inference tooling (e.g., Triton, TensorRT, vLLM, ONNX Runtime).
Ability to design experiments and communicate results clearly.
Preferred / Nice-to-Have Qualifications
Experience deploying production inference systems at scale.
Familiarity with distributed and multi-GPU inference.
Experience contributing to open-source ML or inference frameworks.
Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields.
Experience working close to hardware (CUDA, ROCm, profiling tools).
What Success Looks Like
Measurable gains in latency, throughput, and cost efficiency.
Optimized inference systems running reliably in production.
Research ideas successfully translated into deployable systems.
Clear benchmarks and documentation that inform product decisions.
Relevant Research Areas (Bonus)
Long-context inference optimization
Speculative decoding
KV-cache compression and paging
Efficient decoding strategies
Hardware-aware inference design
Originally posted on Himalayas
Apply for this role Opens jobs.ashbyhq.com — verified as the employer's own application page
Quick question · anonymous · one tap
Would you apply to this job?
Answer to see what other job seekers said.
Your turn · no account needed
Help the next applicant
You may know something about this listing that we cannot see from here. One tap. No account needed. Signed-in reports earn points once the evidence agrees with you.
I know what it pays
Sign in with Google to earn points for reports — 100 confirmed points buy a week of Early Access.
Where this listing came from
- 26 Jul 2026 Himalayas first sighting
Seen on 1 board over 0 days.