AI Research Engineer

Compensation: $147-220k base + 5-15% bonus + long-term incentives

Visa Support: yes for exceptional talent

We are partnered with a Seattle-based AI research institute building fully open AI: large-scale models, datasets, and public artifacts across language, multimodal (vision-language), and agentic systems. With academic freedom and corporate-scale compute, our client offers a rare chance to train frontier open models and share the results with the broader research community.

We are looking for Research Engineers to help train the client's flagship open models. From system design through experiment release, you will own delivery end to end while collaborating closely with research and engineering peers.

Tech stack: Python, PyTorch, JAX, GCP, Docker, CUDA, vLLM, SGLang, RL training frameworks, vision-language models, MoEs, post-training (instruction tuning, RL, reasoning)

• Build and optimize infrastructure for LLM, multimodal, and agentic research: training and inference pipelines, dataset curation, large-scale preprocessing
• Design, train, and evaluate multimodal (vision + language) models and agentic workflows, including tool use, planning, and long-horizon tasks
• Scope and lead research projects, prioritizing experiments for highest impact
• Contribute to the open-source community through model releases, datasets, public APIs, and technical reports

• BS/MS or higher in CS, Math, or a related quantitative field (must-have)
• 4+ years in ML infrastructure and experience training LLMs or multimodal models end-to-end at scale
• Python & PyTorch proficiency (JAX a plus)

• Mixture of Experts (MoE) training, pretraining (language + multimodal)
• Long-sequence models
• Supervised Fine-Tuning (SFT) dataset building
• Reinforcement Learning (RLVR, GRPO, PPO), RL and agent environments
• Reasoning and agentic model training
• CUDA and compute infrastructure optimization

• PhD in ML or equivalent deep learning research experience
• Agentic systems or vision-language model experience
• Cloud infrastructure (GCP/AWS, Docker, distributed training)
• Alignment with open-source AI and a non-profit mission