Senior Research Engineer

Key Responsibilities

• Building and optimizing infrastructure for LLM, multimodal, and agentic research — including training/inference pipelines, dataset curation, and large-scale preprocessing
• Designing, training, and evaluating multimodal models (vision + language) and agentic workflows, including tool use, planning, and long-horizon tasks
• Scoping and leading research projects, prioritizing experiments for highest impact
• Bringing strong software engineering practices to a research environment and bridging cutting-edge work to production-quality products
• Contributing to and supporting the open-source community through model releases, datasets, public APIs, and technical reports

Requirements

• 4+ years of ML infrastructure experience — data preprocessing, model training, evaluation, inference, and deployment
• Experience with end-to-end model development — dataset construction, training, fine-tuning, evaluation, profiling, and monitoring
• Familiarity with modern model architectures — including LLMs (MoEs, long-context models), vision-language models (e.g., Molmo, LLaVA), and experience training and evaluating both
• Agentic systems knowledge — tools, memory, and long-running workflows
• Strong software engineering fundamentals — performant, scalable systems and confident debugging
• Proficiency in Python and a major ML framework (PyTorch, JAX, or TensorFlow)
• Familiarity with cloud and containerization (e.g., GCP, AWS, Docker)
• Strong communication and collaboration skills
• BS or MSc in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field (or equivalent experience)
• A minimum of 2 years of software development experience (or equivalent experience)

Nice-to-Haves

• PhD in ML or equivalent hands-on industry work with deep learning and/or foundation models