Research Engineer - Model Evaluation & MLOps

Research & Systems Collaboration

Must-Haves

• Candidates may be stronger in some areas than others. We are looking for strong software engineering foundations, hands-on ML systems experience, and depth in at least one of model evaluation, MLOps, or model deployment.
• Experience: 2+ years of professional ML or software engineering experience, including work on production ML systems, ML platforms, or MLOps infrastructure.
• Software Engineering: Strong Python and software engineering skills, with experience building reliable production systems.
• Machine Learning Expertise: Hands-on experience with PyTorch, TensorFlow, or JAX and a good understanding of modern language or multimodal model architectures.
• Evaluation & MLOps: Experience with model evaluation or benchmarking and core model lifecycle workflows such as experiment tracking, versioning, deployment, or monitoring.
• GPU Systems: Experience running, benchmarking, and debugging models with one or more GPU inference runtimes, such as vLLM, SGLang, TensorRT-LLM, or equivalent, in containerized cloud or on-premises environments.
• Communication: Ability to document systems clearly and collaborate across research, infrastructure, and product engineering teams.
• Education: MS or PhD in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.

Nice-to-Have

Benefits include

• Medical, dental, and vision insurance
• 401k plan
• Daily lunch, snacks, and beverages
• Flexible time off
• Competitive salary and equity

Equal opportunity