Research Engineer

Designing and implementing machine learning model architectures for specialized domains

Building scalable data pipelines for ingestion, filtering, deduplication, and quality assessment

Developing evaluation frameworks and benchmarks to measure model capabilities

Designing and running end-to-end experiments with rigorous methodology

Training and fine-tuning large-scale machine learning models

Translating research ideas into scalable, deployable systems

Building production-grade infrastructure for model training and inference

Profiling and optimizing model performance across different hardware targets

Building data quality metrics and curation strategies for domain-specific applications

Conducting ablation studies and diagnostic analysis of model behavior

Designing and curating evaluation datasets with statistical rigor

Implementing model compression techniques for efficient deployment

Analyzing and diagnosing failure modes in deployed systems

Working with real-world hardware systems and deployment constraints

Implementing reinforcement learning and preference-based training approaches

Working across hardware, systems, and research domains simultaneously

Correlating pre-training metrics with downstream system-level performance

Developing methods for few-shot adaptation and transfer learning

Architecting bioinformatics and data analysis systems at scale

Designing agentic systems that combine multiple models and tools

Collaborating with cross-functional teams including researchers, engineers, and domain experts

Defining technical direction and research strategy independently

Establishing best practices for code review, testing, and continuous integration

Publishing research findings at top-tier academic venues

Mentoring junior scientists and fostering research culture