AI Research Scientist

Job Description

We are seeking Senior, Lead, Staff, and Principal AI Research Scientists to join our dynamic team. In this role, you will lead the research, development, and evaluation of advanced machine learning and generative AI solutions, transforming cutting‑edge research into production‑ready applications that deliver measurable business value.

You will collaborate with machine learning engineers, data scientists, product managers, domain experts, and engineering teams to design experiments, develop state‑of‑the‑art AI models, establish rigorous evaluation frameworks, and drive the adoption of emerging AI technologies across the organization.

Hybrid in San Jose, CA (3 days on-site per week)

• Design, execute, and analyze machine learning and generative AI experiments using rigorous scientific methodologies.
• Develop strong model baselines, select appropriate evaluation metrics, and perform statistically sound model comparisons.
• Research, evaluate, and adapt state-of-the-art AI techniques for enterprise-specific use cases.
• Design evaluation protocols incorporating offline metrics, online evaluations, user studies, adversarial testing, and red‑team assessments.
• Define data collection, annotation, and labeling strategies while establishing quality assurance processes for training datasets.
• Build reusable research assets including datasets, evaluation frameworks, modular libraries, benchmarking tools, and technical documentation.
• Collaborate with Product Managers, Machine Learning Engineers, and domain experts to translate research findings into scalable production systems.
• Partner with ML Engineering teams to optimize model training, inference performance, deployment pipelines, and production integration.
• Conduct hypothesis‑driven experimentation, ablation studies, and comparative evaluations to improve model quality and reliability.
• Stay current on advancements in artificial intelligence, machine learning, large language models (LLMs), and generative AI research.
• Present technical findings and strategic recommendations to engineering leadership, product teams, and executive stakeholders.
• Contribute to technical publications, patents, conference presentations, and external research communities as appropriate.
• Mentor junior researchers and influence AI research strategy, technical direction, and organizational best practices (Lead, Staff, and Principal levels).

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative discipline strongly preferred.
• Candidates with a Master’s degree and exceptional applied research or industry experience will also be considered.
• Senior Level: 3–5 years of experience in AI or Machine Learning research roles.
• Lead, Staff, and Principal Levels:
  Progressive experience leading AI research initiatives, mentoring technical teams, and driving enterprise AI strategy.
• Demonstrated success delivering research‑driven AI solutions that have been deployed into production environments.
• Experience collaborating across research, engineering, product, and business organizations.
• Strong expertise in:
  • Machine Learning
  • Deep Learning
  • Generative AI
  • Large Language Models (LLMs)
• Hands‑on experience with:
  • Parameter‑Efficient Fine‑Tuning (PEFT)
  • Low‑Rank Adaptation (LoRA)
  • Adapter‑based fine‑tuning
  • Reinforcement Learning from Human Feedback (RLHF)
  • Reinforcement Learning from AI Feedback (RLAIF)
  • PPO, DPO, GRPO, or similar optimization methods
• Strong experience designing statistically sound experiments, ablation studies, and evaluation methodologies.
• Advanced programming proficiency in:
  • Python (preferred)
  • C++
  • Java
• Experience with deep learning frameworks including:
  • Py Torch
  • Hugging Face Transformers
  • Num Py
• Strong mathematical foundation in:
  • Probability
  • Statistics
  • Linear Algebra
  • Calculus
• Ability to read, implement, evaluate, and extend state‑of‑the‑art AI research papers.
• Excellent analytical, communication, collaboration, and technical leadership skills.

• Publications in leading AI and Machine Learning conferences such as:
  • NeurIPS
  • ICML
  • ACL
  • EMNLP
  • CVPR
  • ICLR
• Experience with foundation models, retrieval‑augmented generation (RAG), AI agents, and multimodal AI systems.
• Domain expertise in one or more of the following:
  • Natural Language Processing (NLP)
  • Symbolic reasoning
  • Speech processing
  • Computer vision
  • Reinforcement learning
• Experience with distributed model training, GPU optimization, and large‑scale AI infrastructure.
• Familiarity with MLOps, model deployment pipelines, model monitoring, and production AI systems.
• Experience translating research outcomes into technical roadmaps, product strategy, and enterprise AI initiatives.
• Demonstrated experience mentoring researchers, establishing research standards, and influencing technical direction across organizations.