Member of Technical Staff, Contextual AI
Research Engineer, LLM Systems & Performance
$170-200k
Plus equity
Office located in Mountain View, CA
Contextual language models for enterprise
Be an early applicant
Contextual language models for enterprise
21-100 employees
Be an early applicant
$170-200k
Plus equity
Office located in Mountain View, CA
21-100 employees
Company mission
To change the way the world works through AI.
Role
Who you are
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related technical field (or equivalent practical experience)
- Strong programming skills in Python
- Experience with at least one major ML framework: PyTorch or JAX
- Solid understanding of GPU computing fundamentals (threads/warps/blocks, memory hierarchy, bandwidth vs compute, etc.)
- Familiarity with distributed training or inference concepts (e.g., model parallelism, collective communication, disaggregated serving, KV caching)
- Interest in performance engineering: profiling, kernel fusion, memory layout, and end-to-end system efficiency
- Ability to work in a fast-paced environment, communicate clearly, and collaborate closely with other engineers and researchers
What the job involves
- As a a Member of Technical Staff specializing in Research Engineer – LLM Systems & Performance, you will be part of a small, high-impact team building and optimizing LLM systems end-to-end, from Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) pipelines to high-throughput Inference clusters in production
- You will collaborate closely with researchers and engineers to develop advanced models and infrastructure for the context layer
- Implement and improve components of our SFT and RL training pipelines (e.g., Verl, SkyRL), including data loading, training loops, logging, and evaluation
- Contribute to LLM inference infrastructure (e.g., vLLM, SGLang), including batching, KV-cache management, scheduling, and serving optimizations
- Profile and optimize end-to-end performance (throughput, latency, compute/memory/bandwidth), using tools like Nsight and profilers to identify and fix bottlenecks
- Work with distributed training and inference setups using NCCL, NVLink, and data/tensor/pipeline/expert/context parallelism on multi-GPU clusters
- Help experiment with and productionize quantization (e.g., INT8, FP8, FP4, mixed-precision) for both training and inference
- Write and optimize GPU kernels using tools like CUDA or Triton, and leverage techniques such as FlashAttention and Tensor Cores where appropriate
Our take
Founded by former Facebook research scientists, Contextual AI is building customisable, scalable and trustworthy Large Language Models for use by enterprises. Its main product is a tool to improve model performance so that users can build production-grade AI systems.
The backbone of this product is a technology that feeds curated information into AI models so that the LLM can have more context and up-to-date information. This helps to minimise AI “hallucinations”, an increasingly common problem for models like ChatGPT.
Contextual AI is currently working with companies in the finance, technology and media sectors. Since its impressive 2024 funding, it will no doubt seek growth, perhaps into other sectors. Either way, as AI models become more and more common in enterprise use, the future looks bright for Contextual AI.
Freddie
Company Specialist at Welcome to the Jungle
Company
Funding (2 rounds)
Aug 2024
$80m
SERIES A
Jun 2023
$20m
SEED
Total funding: $100m
Company benefits
- Nnovative Environment: Be part of a forward-thinking company that values creativity and innovation
- Career Growth: Opportunities for professional development and career advancement
- Collaborative Culture: Work with a talented and supportive team dedicated to excellence
Company HQ
SoMa, San Francisco, CA
Leadership
Douwe Kiela
(CEO)Previously Adjunct Professor at Stanford University, Head of Research at Hugging Face and Research Scientist at Facebook. Was also the CEO and Founder of Brainstorm IT.
Amanpreet Singh
(CTO)Previously Research Team Lead at Hugging Face, Research Engineer at Facebook AI, and Mobile Maintainer at jQuery Foundation. Was also a Software Engineering Intern at Amazon.
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