Senior Research Engineer

Senior Research Engineer

Location: Seattle, WA Company Stage of Funding: Nonprofit AI Research Institute / Growth Stage Office Type: Hybrid — On-site in Seattle Salary: $147,000 – $220,000 + Competitive Benefits Equity: N/A — Nonprofit Organization Visa: Open to Visa Transfers and New Visa Sponsorships — OPT, H-1B transfers, new H-1B, TN, and other sponsorship pathways supported Travel: Seattle-based; relocation support may be available

Company Description

Our client is a nonprofit AI research institute focused on developing open-source foundational AI models, datasets, and research artifacts.

The organization sits at the intersection of academic research, frontier AI labs, and startup-style engineering, giving researchers and engineers the resources to work on large-scale AI systems while maintaining significant freedom to publish, collaborate, and openly share their work.

The team develops and releases large open models across language, multimodal, reasoning, and agentic AI, with flagship model families and research projects spanning pretraining, post-training, reinforcement learning, evaluation, datasets, and model infrastructure.

The organization has access to substantial compute resources and large-scale GPU infrastructure, allowing Research Engineers to work on problems that require significant distributed training and systems expertise.

The team is intentionally relatively small compared with large commercial AI labs, giving engineers substantial ownership and direct collaboration with researchers and technical leadership.

The Senior Research Engineer will work closely with leading AI researchers and engineers to build and train foundation models at scale, contributing across the model lifecycle from data and infrastructure through pretraining, post-training, evaluation, and release.

The ideal candidate is someone who has actually built and trained foundation models, rather than someone whose experience is primarily using existing models through APIs, RAG systems, or domain-specific fine-tuning.

What You Will Do

• Foundation Model Training & Research Engineering

• Build, train, evaluate, and improve large language and multimodal foundation models

• Work on end-to-end model development across pretraining and post-training

• Design and implement large-scale training pipelines

• Develop infrastructure for distributed model training and experimentation

• Work with modern foundation-model architectures including transformer-based models and mixture-of-experts systems

• Develop and optimize training systems running across large GPU clusters

• Investigate model behavior through experiments, evaluations, and systematic analysis

• Translate research ideas into working implementations and large-scale experiments

• Collaborate closely with research scientists and engineers on model development

• Own research-engineering projects from initial scoping through implementation, experimentation, and release

• Identify high-impact technical and research problems and develop practical solutions

• Contribute to improvements across the full model-development lifecycle

• Work on model architectures, training methods, datasets, infrastructure, and evaluation

• Build systems that enable researchers to run large-scale experiments efficiently

• Pretraining, Post-Training & Reinforcement Learning

• Contribute to pretraining language and multimodal foundation models

• Build and improve pretraining datasets and data-processing pipelines

• Work on supervised fine-tuning and instruction-tuning datasets

• Develop post-training pipelines for reasoning and instruction-following models

• Work with reinforcement learning approaches including RL, RLVR, GRPO, PPO, and related methods

• Build environments and infrastructure for reinforcement learning

• Develop training environments and sandboxing systems for agentic models

• Work on synthetic data generation and data-quality pipelines

• Contribute to distillation and other model-improvement techniques

• Design experiments to improve model capabilities and behavior

• Develop evaluation frameworks for measuring model performance

• Investigate training failures and model-quality issues

• Work across different stages of the model lifecycle depending on project needs

• Contribute to emerging approaches for reasoning and agentic model training

• Multimodal & Agentic AI

• Work on multimodal models combining vision and language

• Develop and train vision-language models

• Contribute to multimodal pretraining and post-training

• Build systems for tool use, planning, and long-horizon agentic tasks

• Develop reinforcement-learning environments for agents

• Build infrastructure for agentic training and evaluation

• Work on models capable of reasoning through complex tasks

• Investigate model behavior across multimodal and agentic settings

• Develop datasets and evaluation systems for multimodal and agentic capabilities

• Work with emerging model architectures and training approaches

• Collaborate with researchers exploring new capabilities in reasoning, agents, and multimodal AI

• ML Infrastructure & Systems Engineering

• Build and optimize infrastructure supporting large-scale model training

• Develop distributed training systems for large GPU clusters

• Optimize training workloads for performance, reliability, and compute efficiency

• Work with CUDA and GPU infrastructure where applicable

• Improve training throughput and resource utilization

• Build large-scale data-processing and preprocessing pipelines

• Develop systems for dataset curation and preparation

• Work with cloud infrastructure including GCP and/or AWS

• Build and operate containerized ML infrastructure using Docker

• Work with distributed computing environments

• Deploy and serve large language and multimodal models

• Work with model-serving technologies such as vLLM and SGLang

• Diagnose infrastructure and training bottlenecks

• Improve the reliability and scalability of research infrastructure

• Contribute to engineering standards and tooling used across research teams

• Bridge research prototypes into production-quality systems

• Develop reusable infrastructure that enables researchers to iterate faster

• Research Leadership & Open-Source Engineering

• Scope and lead research-engineering projects

• Prioritize experiments based on expected research and technical impact

• Take ownership of projects from concept through implementation and release

• Collaborate closely with research scientists, engineers, and technical leadership

• Communicate technical findings and research insights clearly

• Participate in technical discussions, design reviews, and research planning

• Contribute to open-source models, datasets, libraries, and technical artifacts

• Support public model releases and research publications

• Develop technical reports and documentation

• Share findings with the broader research and engineering community

• Contribute to a culture of open research and transparent collaboration

• Help shape the direction of future foundation-model research

• Work flexibly across different parts of the ML stack based on team needs

Ideal Candidate Background

Experience Requirements

• 6+ years of general software engineering experience
• 4+ years of experience in machine learning infrastructure and/or large-scale model training
• Strong hands-on experience building and training foundation models
• Experience training LLMs and/or multimodal models end-to-end at scale
• Experience working on large-scale deep learning systems in production or research environments
• Strong software engineering fundamentals alongside deep ML expertise
• Experience owning technically complex projects from design through implementation and experimentation
• Experience working closely with research scientists and ML engineers
• Experience operating in highly technical, research-oriented environments
• Experience working with large GPU clusters or distributed training infrastructure
• Experience with one or more stages of the foundation-model lifecycle
• Strong experience with at least one of:
• Foundation-model pretraining
• Multimodal model training
• Post-training
• Reinforcement learning
• Model architecture
• Pretraining data
• Agentic model training
• Synthetic data
• Training infrastructure optimization
• CUDA/compute optimization
• Experience at a leading AI lab, research organization, or technically strong AI company is strongly preferred
• Experience at organizations such as Google, Meta, OpenAI, Anthropic, DeepMind, NVIDIA, or comparable AI research