MLOps Engineer, LLM Systems, Serving, GPU Kernels, Profiling

Job not on LinkedIn

🔥 5 minutes ago

🌐 United States, Canada, +1 more countries – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote 🇬🇧 United Kingdom – Remote

💵 $90 - $120 / hour

⏱ Part Time

🟢 Junior

🟡 Mid-level

🗣️ LLM Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 20%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

💼 Consulting

👥 HR Tech

☁️ SaaS

Consulting • HR Tech • SaaS

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Design challenging, domain-relevant tasks across GPU kernels, performance profiling, debugging, and inference serving, and write accurate, well-structured solutions • Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems, training infrastructure, and framework-level topics • Evaluate MLOps and ML systems tasks and solutions and provide clear, written technical feedback • Develop guidelines and detailed rubrics or evaluation frameworks covering kernel-level optimization, profiler output interpretation, distributed systems reasoning, and serving throughput and latency trade-offs • Collaborate with subject matter experts to keep training data consistent and accurate • Contribute to AI model training and evaluation work by writing and assessing MLOps and ML systems tasks and solutions for frontier AI training data

🎯 Requirements

• 2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or GPU and accelerator performance engineering • Practical experience in at least one of: writing or optimizing custom GPU kernels (CUDA, Triton, Pallas); performance profiling and trace analysis (Kineto, torch.profiler, Nsight, XLA or JAX profiler); debugging distributed or accelerator-bound workloads; serving large language models at scale (vLLM, SGLang, TensorRT-LLM, Ray Serve, KV cache, paged attention, continuous batching) • Working production experience with JAX and/or PyTorch • Familiarity with modern accelerators such as A100, H100, B200 or TPU • Ability to reason about throughput, latency and memory trade-offs • Demonstrable career progression • Ability to engage reliably for at least 40 hours/week during weekdays • Strong written communication skills and ability to explain complex technical decisions clearly