Machine learning serp_jobs.h1.location_city

This range is provided by RecruitSeq. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$200,000.00 / yr - $600,000.00 / yr

Member of Technical Staff, Machine Learning

Our client is a cutting-edge AI startup in the Bay Area developing highly efficient foundational models for real-world deployment across devices. Rapidly growing, highly technical team focused on building top-tier large language model (LLM) architectures with real-world impact.

As a Member of Technical Staff, you’ll drive innovation on large-scale model training, infrastructure, and optimization. You’ll collaborate closely with a small team of seasoned researchers and engineers, advancing state-of-the-art LLMs for efficient deployment at scale.

Responsibilities

Design, implement, and optimize large-scale pretraining and post-training pipelines for language models

Tackle challenges in model parallelism, distributed training, and low-level hardware / software co-design

Monitor, maintain, and troubleshoot massive training and inference workloads end-to-end

Collaborate on advancing core model architectures, inference optimizations, and custom hardware design

Contribute to open-source community initiatives and research publications

Analyze and streamline data pipelines, instruction data curation, and evaluation methods

Apply advanced optimization theory to improve model performance

Qualifications

Degree in Computer Science, Electrical Engineering, or related technical field (or equivalent practical experience)

Hands‑on experience in machine learning research centered on LLMs, efficient AI systems, or large‑scale model training

Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)

Expertise with distributed training, parallelization strategies, and large‑scale computational infrastructure

Understanding of low‑level GPU optimizations, CUDA, or similar technologies

Preferred Skills

Previous work at leading research labs or high‑impact contributions community AI projects

Experience with custom hardware, FPGA / ASIC design, or maximizing training throughput

Familiarity with open‑source inference engines (e.g., llama.cpp, vllm, triton)

Academic publications in optimization, LLM training, or AI infrastructure

Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function

Engineering and Research

Industries : Staffing and Recruiting and Software Development

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