Research Engineer, Alameda

Research Engineer, Foundation Models
About the Opportunity
We are seeking a Research Engineer to help advance the next generation of large-scale AI systems.
This role sits at the intersection of research and engineering, focusing on the development, training, evaluation, and deployment of state-of-the-art machine learning models.
You will work across the full model lifecycle, from building large-scale datasets and training infrastructure to experimenting with new model architectures and inference techniques.
This is an opportunity to contribute directly to cutting-edge work in large language models, reinforcement learning, long-context systems, and scalable AI infrastructure.
Responsibilities
Develop and optimize training, evaluation, and deployment pipelines for large-scale AI models
Improve inference efficiency, latency, and throughput across advanced model architectures
Design and maintain research and production frameworks used for model development
Train and scale foundation models across large distributed GPU environments
Build and manage large-scale data processing, collection, and curation pipelines
Create high-quality datasets to improve model performance and targeted capabilities
Research, prototype, and benchmark novel model architectures and training approaches
Contribute to experimentation in areas such as reinforcement learning, long-context modeling, reasoning systems, and inference optimization
Collaborate closely with researchers and engineers to transition ideas from experimentation to production
Qualifications
Required
Strong software engineering and systems development experience
Deep understanding of modern machine learning and deep learning techniques
Experience training, fine-tuning, or evaluating large language models
Familiarity with distributed computing and large-scale infrastructure
Experience building and maintaining data pipelines and ETL workflows
Ability to design experiments, analyze results, and iterate on research directions
Strong problem-solving skills and a research-oriented mindset
Preferred
Experience working with large GPU clusters and distributed training frameworks
Background in model optimization, inference systems, or AI infrastructure
Contributions to machine learning research, open-source projects, or published work
Experience with reinforcement learning, long-context models, or large-scale data systems
What We Value
Ownership and accountability
Strong collaboration and communication skills
Bias toward execution and practical problem-solving
Intellectual curiosity and continuous learning
High standards for technical excellence and product quality
Ability to thrive in fast-moving, high-impact environments
Compensation & Benefits
Competitive base salary and equity package
Comprehensive medical, dental, and vision coverage
401(k) program with employer matching
Flexible paid time off policy
Relocation assistance and visa sponsorship, where applicable
Opportunity to work alongside a highly talented and mission-driven team
Access to cutting-edge infrastructure and research resources
Keywords:
Machine Learning, Artificial Intelligence, Deep Learning, Large Language Models, LLMs, Foundation Models, Generative AI, Applied AI, AI Research, Research Engineering, Model Training, Distributed Training, Pretraining, Fine-Tuning, Post-Training, Reinforcement Learning, RLHF, Reinforcement Learning from Human Feedback, Inference Optimization, Model Serving, Model Evaluation, Long Context Models, Reasoning Models, AI Infrastructure, GPU Clusters, High Performance Computing, HPC, Distributed Systems, CUDA, PyTorch, JAX, TensorFlow, Neural Networks, Transformer Models, Retrieval Augmented Generation, RAG, Synthetic Data, Data Engineering, Data Pipelines, ETL, Data Processing, Web Crawling, Data Collection, Feature Engineering, MLOps, ML Systems, Scalable Systems, Parallel Computing, Model Architecture Design, Experimentation, Research Scientists, Research Engineers, Software Engineering, Backend Engineering, Performance Optimization, Production ML, AI Agents, Agentic AI, Autonomous Systems, Prompt Engineering, Multi-Agent Systems, Vector Databases, Embeddings, Quantization, Model Compression, Infrastructure Engineering, Cloud Computing, Kubernetes, Python, C++, Open Source AI, Frontier Models, Applied Research, Statistical Learning, Computer Science, Algorithms, Large Scale Computing, Model Alignment, AI Safety, Training Infrastructure, Compute Optimization, Inference Systems, Foundation Model Research, Machine Learning Infrastructure, AI Platform Engineering, Systems Engineering, Data Infrastructure, Production Systems, Scalable AI Systems, Research & Development, Advanced AI Systems, Emerging Technologies, Distributed Computing, GPU Optimization, AI Product Development,