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/engineering environments is a strong signal
• PhD in ML, CS, or a related quantitative field is preferred but not required with equivalent deep learning research experience
• Experience contributing to open-source AI research or publications is strongly preferred
• Must be based in or willing to relocate to Seattle
• Must be comfortable working on-site in Seattle

Technical Requirements

• Expert-level Python
• Strong PyTorch experience
• Experience training large-scale deep learning models
• Experience with modern LLM architectures
• Experience with transformer-based models
• Understanding of mixture-of-experts architectures
• Experience with large-scale model pretraining and/or post-training
• Experience with distributed training
• Experience with GPU computing
• Experience with CUDA preferred
• Experience with ML infrastructure
• Experience with large-scale data processing
• Experience building training pipelines
• Experience with model evaluation
• Experience with reinforcement learning preferred
• Experience with RLVR, GRPO, PPO, OPD, or related methods preferred
• Experience building RL environments preferred
• Experience with agentic training preferred
• Experience with multimodal or vision-language models preferred
• Experience with synthetic data generation preferred
• Experience with long-context or long-sequence models preferred
• Experience with supervised fine-tuning datasets
• Experience with instruction tuning
• Experience with model distillation
• Experience with cloud infrastructure such as GCP or AWS
• Experience with Docker
• Experience with distributed compute infrastructure
• Experience deploying or serving LLMs
• Experience with vLLM or SGLang preferred
• JAX experience is a plus
• Experience with modern RL training frameworks such as SkyRL or Slime is a plus
• Strong understanding of model training performance and compute efficiency
• Strong software engineering and testing practices
• Ability to work across the full ML stack

Product & Research Engineering Requirements

• Strong understanding of the full foundation-model lifecycle
• Ability to translate research ideas into working software
• Ability to design experiments and evaluate results
• Ability to identify high-impact research and engineering problems
• Strong research intuition combined with practical engineering ability
• Ability to move between research and production-quality engineering
• Comfortable working with ambiguous research questions
• Ability to build systems that support rapid experimentation
• Strong understanding of model behavior and evaluation
• Ability to reason about tradeoffs between model quality, compute, data, and engineering complexity
• Comfortable working closely with research scientists
• Ability to communicate research insights clearly
• Strong interest in open AI research
• Ability to contribute to model releases, datasets, APIs, and technical reports
• Comfortable working across infrastructure, training, data, evaluation, and modeling
• Ability to take ownership of entire technical projects
• Strong interest in advancing foundation-model capabilities
• Comfortable learning and applying new research techniques quickly

Soft Skills

• Extremely high ownership and accountability
• Strong technical judgment
• Highly proactive and self-directed
• Comfortable operating with ambiguity
• Excellent written and verbal communication
• Strong research and problem-solving ability
• Strong analytical thinking
• Low-ego and collaborative working style
• Comfortable participating in technical and research debates
• Able to express strong technical opinions while remaining open-minded
• Comfortable receiving and incorporating feedback
• Strong attention to detail
• Comfortable making decisions with incomplete information
• Strong bias toward experimentation and execution
• Comfortable working closely with research scientists
• Comfortable working across different parts of the ML stack
• Strong intellectual curiosity
• Fast learner
• Comfortable working in rapidly evolving AI research areas
• Comfortable owning projects independently
• Strong ability to explain complex technical concepts clearly
• Comfortable presenting research findings to technical audiences
• Resilient and adaptable
• Motivated by difficult technical problems
• Comfortable working in a small, highly technical team
• Strong collaborative instincts
• Comfortable contributing outside a narrowly defined specialty
• Strong interest in open-source AI
• Motivated by advancing the field rather than simply shipping closed commercial products

Compensation & Benefits

• Base salary: $147,000 – $220,000
• Competitive benefits
• Nonprofit organization with access to significant research resources and compute
• Open to visa transfers including OPT and H-1B transfers
• Open to new visa sponsorships including H-1B and TN
• E-Verify participant
• Welcomes applicants from outside the United States
• Opportunity to work with substantial GPU and compute resources
• Opportunity to train frontier-scale open foundation models
• Direct collaboration with leading AI researchers and engineers
• Significant technical and research ownership
• Opportunity to publish and openly share research
• Opportunity to contribute to open-source AI models and datasets
• Opportunity to work across pretraining, post-training, RL, evaluation, and infrastructure
• Opportunity to work on language, multimodal, reasoning, and agentic models
• Opportunity to work closely with researchers from leading universities and AI organizations
• Strong learning environment with access to technical talks and AI experts
• Collaborative and transparent research culture
• Healthy work/life balance
• Generous paid vacation and sick leave
• Family leave
• Opportunity to work at the intersection of academia, frontier AI labs, and startups
• Small-team environment with significant ownership and autonomy

Why Join

This is an opportunity to work on foundation models at meaningful scale while retaining the research freedom and openness of an academic environment.

You’ll join a small, highly technical team working on large language, multimodal, reasoning, and agentic models, with access to substantial compute and GPU resources.