Title: AI Research Scientist
Location: San Jose, CA
Responsibilities:
- Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics.
- Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases.
- Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness.
- Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes.
- Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints.
- Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation.
- Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems.
- Contribute to academic publications and represent the company in research communities, as needed.
Educational Qualifications:
- Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred.
- Candidates with a master’s degree and exceptional research or industry experience will also be considered.
Industry Experience:
- 3–5 years of experience in AI/ML research roles, ideally in applied or product-focused environments.
- Demonstrated success in delivering research-driven solutions that have been deployed in production.
- Experience collaborating in cross-functional teams across research, engineering, and product.
- Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus.
Technical Skills:
- Strong foundational knowledge in machine learning and deep learning algorithms.
- Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO).
- Ability to read, implement, and adapt state-of-the-art research papers to real-world use cases.
- Proficiency in hypothesis-driven experimentation, ablation studies, and statistically sound evaluations.
- Advanced programming skills in Python (preferred), C++, or Java.
- Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc.
- Strong mathematical foundations in probability, linear algebra, and calculus.
- Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc.
- Ability to translate research insights into roadmaps, technical specifications, and product improvements.