Member of Technical Staff, Applied Research

The Applied Researcher role is designed for engineers who love working across ML, systems, and real-world products, and thrive on working directly with customers to bring advanced models into production.

About the Role

As an Applied Researcher, you will sit at the intersection of ML research, systems engineering, and customer-facing problem solving. You’ll work hands-on with customers and customer data to tune, evaluate and deploy models using various techniques such as SFT / DPO / RL, to help customers build competitive models using their unique data tailored to their unique products.

You will be the technical bridge between customer needs, customer data, and our tuning and serving infrastructure, helping shape the future of applied AI.

Minimum Qualifications

• BS/MS in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent practical experience, open to all levels of experiences.
• Strong experience with PyTorch and modern Transformer architectures.
• Solid computer science fundamentals: data structures, algorithms, concurrency, distributed systems, networking.
• Hands-on experience training, fine-tuning, or evaluating machine learning models, preferably LLMs.
• Familiarity with recent developments in the LLM research domain, including model architectures, training methods, and evaluation strategies.
• Passion for partnering with customers: understanding their constraints, co-designing solutions, and iterating based on real-world feedback.
• Curiosity and enthusiasm for exploring a wide range of problem domains and project types - from quick experiments to long-running, complex engagements.
• Ability to operate in a fast-paced, ambiguous environment and drive projects independently.

Preferred Qualifications

• Experience working directly with customers to deliver end-to-end modeling solutions, from understanding their data and product requirements to deploying tuned models in production.
• Strong familiarity with evaluation methodologies for LLMs (benchmarks, custom evals, error analysis).
• Proficiency in diagnosing system-wide problems that hinder customers from achieving desirable outcomes.
• Deep understanding of tuning techniques (SFT, DPO, RL) and the underlying mathematical principles.
• Knowledge of infrastructural components that enterprises commonly use, such as databricks, S3/GCS storage, SageMaker, artifact registry etc
• Familiarity with cloud-native tooling (containers, Docker, Kubernetes, or similar).