Research Engineer

Implementing and optimizing machine learning models across the full training and inference pipeline

Designing and conducting rigorous scientific experiments to evaluate model capabilities and performance

Translating research prototypes into production-ready systems and infrastructure

Developing scalable distributed systems for large-scale model training and inference

Optimizing computational performance through low-level systems programming and hardware-aware algorithms

Evaluating and benchmarking model capabilities with domain-specific metrics and evaluation frameworks

Building and maintaining robust data pipelines for preprocessing, curation, and generation at scale

Developing novel algorithms and techniques for reinforcement learning and model training

Developing agentic systems that can autonomously perform multi-step complex tasks

Interpreting and understanding the internal mechanisms and behavior of deep learning models

Designing and implementing multimodal learning systems that integrate multiple data modalities

Implementing techniques for efficient model compression, quantization, and sparsity

Building systems to evaluate and ensure safety, security, and reliability of AI agents

Collaborating across multidisciplinary teams to integrate research insights into products

Leading research projects with clear problem definition, hypothesis formation, and iterative refinement

Communicating complex technical concepts and research findings clearly to diverse audiences

Balancing research ambitions with practical engineering constraints and timelines