Member Of Technical Staff, Applied Research (AI)

TL;DR

Member Of Technical Staff, Applied Research (AI): Building and deploying advanced generative AI models for customer products with an accent on fine-tuning techniques and model evaluation. Focus on bridging the gap between customer data and inference infrastructure to solve real-world scalability and performance challenges.

Location: San Mateo, CA

Company

A fast-growing, Series C generative AI infrastructure company dedicated to high-performance model serving and LLM innovation.

What you will do

• Collaborate directly with customers to understand their unique data and product requirements.
• Tune and deploy models using SFT, DPO, and RL techniques tailored to specific use cases.
• Develop and implement robust evaluation methodologies for LLMs, including benchmarks and custom evals.
• Bridge the gap between customer-facing problems and internal model-serving infrastructure.
• Diagnose and resolve system-wide performance and modeling issues to ensure production success.

Requirements

• Strong experience with PyTorch and modern Transformer architectures.
• Solid foundation in computer science, including concurrency, distributed systems, and data structures.
• Hands-on experience in training, fine-tuning, or evaluating machine learning models.
• Familiarity with current LLM research, model architectures, and training methodologies.
• Proven ability to partner with customers to iterate on solutions based on real-world feedback.
• Ability to operate effectively in a fast-paced, ambiguous environment.

Nice to have

• Deep expertise in tuning techniques like SFT, DPO, and RL.
• Experience with cloud-native infrastructure including Docker, Kubernetes, and enterprise data storage.
• Proficiency with LLM benchmarking and error analysis.
• Knowledge of enterprise infrastructure components like Databricks or SageMaker.

Culture & Benefits

• Direct impact on global AI infrastructure development.
• Collaborative environment focused on results and innovation without bureaucracy.
• Opportunity to work with leading experts in ML and systems engineering.
• Exposure to bleeding-edge AI models and inference speed technologies.