AI Research Scientist

• Design and implement end-to-end model pipelines to production across multiple product domains, including ranking, recommendations, search, and personalization
• Develop and maintain scalable data pipelines, data quality checks, and model monitoring to ensure reliability, performance, and responsible behavior of ML systems in production
• Collaborate with cross-functional partners (product, analytics, engineering) to translate ambiguous business needs into well-scoped ML projects, communicate findings, and influence decision making with data-driven insights
• Use A/B tests and offline/online evaluation frameworks to measure model impact and guide iterative improvement

Machine Learning Scientist III – Whole Trip AI

• Frame decision problems, not just research questions; clarify decisions, assumptions, and key tradeoffs.
• Assess project scope, form appropriate research questions, and identify appropriate research methods (developing new methods if/where needed and using GenAI tools to accelerate research workflows).
• Run research studies from start to finish, including developing a realistic research plan and materials, recruiting participants, and synthesizing data into actionable insights.
• Build alliances across the company to identify new partners, drive teamwork, and deeply understand the business context of research problems.
• Advocate for the customer by articulating research findings and product ideas through clear and insightful presentations.
• Translate insights into concise, executive-ready narratives that foster alignment across product, design, and cross-functional leadership partners.
• Mentor junior staff and supervise contractors/vendors on more complicated programs of study.

Staff UX Researcher, AI

• Turn ambiguous healthcare, product, and operational problems into well-posed ML, AI, ranking, optimization, NLP, or LLM-based tasks
• Build strong baselines and improve on them efficiently using the right modeling approach for the problem
• Develop models across traditional ML, deep learning, NLP, and LLM-based approaches where appropriate
• Design offline and online evaluations that are honest, measurable, and predictive of real-world impact
• Choose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes
• Run careful error analysis and use it to improve model quality, product fit, and operational usefulness
• Identify label leakage, selection bias, confounding, and other data artifacts before they reach production
• Explore messy real-world data, assess label quality, and determine whether a problem is ready for modeling
• Partner with ML engineering to productionize models reliably and define what production-readiness requires
• Work with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge cases
• Explain model tradeoffs, uncertainty, limitations, and expected impact clearly to product, operations, clinical, and leadership teams
• Write experiment docs, summarize findings, and help teams make informed decisions about when and how to deploy AI systems
• Pressure-test whether results are real, robust, and useful before recommending production use

Applied Scientist, AI

• Contribute to ongoing research projects and help define new research directions
• Develop internal prototypes to test and evaluate methods relevant to embodied AI assurance
• Document research findings in the form of reports, presentations, and scientific publications

AI Research Internship

• Contribute to ongoing research projects and help define new research directions
• Engage with universities to maintain and strengthen ongoing and future research collaborations
• Develop internal prototypes to test and evaluate methods relevant to embodied AI assurance
• Publish scientific results and attend conferences to build new knowledge and share DNV’s work

AI Researcher

• Develop AI/ML models to predict structural biomolecular interactions for novel modalities leveraging protein-protein interactions.
• Design innovative AI/ML approaches to predict protein cooperativity, affinity & other biological properties based on structures.
• Develop co-folding models that incorporate structural and non-structural priors using combinations of public and proprietary BMS data.
• Develop AI/ML models that leverage chemoproteomics data contributing to the generation of novel druggability hypotheses for hard-to-drug targets.
• Author scientific reports, and present methods, results, and conclusions to publishable standard.
• Contribute to the planning and execution of collaborative projects with leading academic and commercial research groups worldwide.

Senior Scientist, AI Computational Structural Biology

• Conduct original research in embodied AI, including learning from warfighter demonstration, reinforcement learning, memory, reward, vision-language modeling, and world modeling
• Build, test, and benchmark large-scale models for perception, decision-making, and control in simulated and physical environments
• Investigate transfer learning and continual learning paradigms across diverse robotic domains
• Collaborate with engineering, robotics, and field teams to integrate research into operational systems
• Lead research strategy and roadmap across key areas of autonomy and machine learning
• Support field tests and data collection campaigns to evaluate system performance in realistic environments

Senior AI Researcher

• Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration.
• Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks.
• Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation.
• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities.
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence, such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses.
• Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence.
• Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning.
• Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions.
• Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives.
• Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS).
• Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery.
• Co-author manuscripts, concept papers, and translational strategy documents.

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

• Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration
• Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks
• Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation
• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses
• Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence
• Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning
• Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions
• Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives
• Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS)
• Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery
• Co-author manuscripts, concept papers, and translational strategy documents

• Design and train large scale transformer and hybrid foundation models
• Own model architecture choices across text, multimodal, and emerging paradigms
• Diagnose and resolve training instabilities at scale
• Navigate scaling tradeoffs across data and compute
• Apply strong fundamentals in deep learning and representation learning
• Build models from first principles, not just adapt pre-existing codebases
• Own optimiser and scheduler choices, including AdamW, Lion, Adafactor
• Design and experiment with loss functions including next-token prediction and contrastive objectives
• Design and execute large-scale training using FSDP, ZeRO-3, Tensor parallelism, Pipeline parallelism
• Apply mixed precision (bf16, fp8) and gradient checkpointing

Senior AI Scientist

• Research and develop approaches for continual learning, fast adaptation, memory formation, knowledge retention, and transfer learning in large-scale AI systems.
• Own the full development pipeline from data generation to evaluation to product integration.
• Contribute to both research strategy and technical implementation—this is a hands-on role. Have ownership over your own applied research stream.
• Investigate topics such as continual learning, online learning, test-time adaptation, memory architectures, meta-learning, self-improvement, and knowledge consolidation.

AI Research Scientist/Manager – Adaptive Intelligence

• Research and develop new approaches for spatial reasoning, geometric understanding, physical reasoning, and structured world modeling in large-scale AI systems.
• Own the full development pipeline from data generation to evaluation to product integration.
• Contribute to both research strategy and technical implementation—this is a hands-on role. Have ownership over your own applied research stream.
• Investigate topics such as 3D reasoning, geometric representations, multimodal reasoning, world models, simulation, and physical intuition.
• Stay on the edge of what’s possible and bring promising ideas into reality (e.g. following the literature, attending conferences, etc).

AI Research Scientist/Manager – Spatial Reasoning

• Design, develop, and deploy advanced generative AI workflows and multi-agent conversational architectures (e.g., using LangGraph) to power personalized user interactions and automated symptom triage
• Implement, build on, and augment existing LLM/NLP tools to automate the abstraction of high-priority clinical variables and derived features from unstructured medical text, maximizing data completeness and accuracy
• Develop automated evaluation pipelines and 'LLM-as-a-judge' grading rubrics to continuously benchmark agent safety, accuracy, factuality, and compliance across model updates, enabling self-optimization cycles
• Handle real-world data challenges from clinical and remote settings, ensuring absolute safe data boundaries, privacy preservation, and rigorous technical validation before production releases
• Communicate highly technical results, methods, and evaluation frameworks clearly via presentations and well-structured reports to both technical and non-technical cross-functional audiences

Staff AI Applied Scientist

• Conduct fundamental and innovative development in low-cost yet powerful vision-language models (VLM), unified models, automatic model compression, optimization and deployment on cloud and edge.
• Design or implement state-of-the-art techs on model compression, inference speedup, deployment on hardwares, tool automation.
• PoC for various vision+text, generation relevant tasks (VQA, captioning, understanding, etc) and hardwares.
• Contribute to library and tool development to support business; or Publish influential research in top-tier conferences and journals.

Research Intern, Vision Foundation Model and Generative AI

• Invent, design, and implement efficient algorithms
• Optimize large language model inference on DNN accelerators
• Work as part of a close-knit team of mathematicians, ML researchers, and ML engineers
• Create and apply advanced algorithmic and numerical techniques

Senior Staff ML Researcher – LLM Algorithmic Optimization

• Design and implement end-to-end operational systems for research workflows — from intake and participant recruitment to insight synthesis.
• Own participant recruitment and panel management; build and maintain relationships with users, prospects, and legal professionals
• Set up, evaluate, and manage the research repository and tech stack, with a focus on AI-powered tooling: automated synthesis, AI repository and intake systems, and systematizing self-serve and rolling research to improves team velocity and institutional memory.
• Develop templates, intake processes, and educational documentation to help the team run studies faster and more consistently.
• Build scalable processes for budget and resource allocation, capacity planning, and project tracking.
• Define success metrics and reporting frameworks that give leadership visibility into team health, output quality, research velocity, and coverage gaps
• Identify where AI reduces friction in the research workflow — from recruitment to readout, and implement or build those improvements.
• Provide guidance, review and help coordinate rolling research schedules: Qualitative user interviews, usability tests, concept tests, often on tight timelines.
• Support repository of findings into clear, actionable reports for cross-functional team discovery and use.

AI Research Operations Lead

• Research, develop, and deploy advanced AI systems
• Focus on large language models
• Focus on agentic AI
• Focus on multimodal learning
• Focus on enterprise-scale AI applications
• Design experiments and develop new methods
• Turn research insights into impactful AI solutions
• Write scientific articles for top-tier machine learning and AI conferences
• Publish technical blog posts
• Contribute to the open-source AI community

• Contribute to sophisticated research in large language models (LLMs), natural language processing (NLP), and multimodal deep learning
• Help build, implement, and evaluate machine learning models and algorithms
• Participate in data collection, preprocessing, and analysis
• Assist in developing and fine-tuning AI models for various applications
• Create experiments, compare algorithms, tune hyperparameters, and document results
• Share findings with the team, contribute to reports and presentations, and help onboard new team members

AI Research Scientist, PhD – Intern

• Lead an independent, high-impact research agenda on large language models and agentic systems, owning projects from early hypothesis through model training, evaluation, and production deployment
• Design and execute large-scale post-training experiments using supervised fine-tuning, reinforcement learning from human feedback (RLHF), RLAIF, DPO, and emerging alignment techniques — with a focus on improving multi-step reasoning, planning, and tool use in enterprise agentic workflows
• Build novel evaluation benchmarks and methodologies that push beyond existing limitations, establishing rigorous measures for how well models perform on complex, real-world enterprise tasks
• Develop scalable data synthesis and curation pipelines that generate the high-quality training signal driving model improvement — including LLM-as-judge frameworks, synthetic data generation, and adversarial dataset construction
• Shape WRITER's model architecture and training roadmap by translating your research insights into concrete improvements to our enterprise-grade LLMs, working hand-in-hand with research engineering and product teams
• Publish and present original research at top-tier venues — NeurIPS, ICLR, ICML, ACL, and others — representing WRITER at the frontier of the field and contributing to the broader scientific community
• Mentor and uplevel fellow researchers and engineers on the team, helping set a high bar for scientific rigor, experimental design, and research culture

Staff AI Research Scientist

• Lead end-to-end research projects in Agentic AI / AI / Generative AI / Machine Learning from ideation, research, experimentation, implementation, evaluation, transfer and documentation.
• Contribute and lead building the next generation of foundation models and agentic AI systems trained / operating on large volumes of heterogeneous data - both structured and unstructured - that enhance Wells Fargo's products, services, and operations.
• Lead the deployment of foundation models, LLMs, and agentic AI systems into production, with a strong understanding of deployment trade-offs.
• Drive the exploration, application, and rigorous evaluation of emerging AI technologies that solve real-world challenges.
• Collaborate closely with interdisciplinary teams, including researchers, data scientists, applied engineers, and domain experts.
• Translate research insights into impactful business solutions, open-source contributions, patents, and publications.
• Publish in top-tier AI/ML conferences and journals and represent Wells Fargo in the broader AI research community.

AI Research Scientist jobs in California

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Machine Learning Scientist III – Whole Trip AI

Staff UX Researcher, AI

Applied Scientist, AI

AI Research Internship

AI Researcher

Senior Scientist, AI Computational Structural Biology

Senior AI Researcher

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Senior AI Scientist

AI Research Scientist/Manager – Adaptive Intelligence

AI Research Scientist/Manager – Spatial Reasoning

Staff AI Applied Scientist

Research Intern, Vision Foundation Model and Generative AI

Senior Staff ML Researcher – LLM Algorithmic Optimization

AI Research Operations Lead

AI Researcher

AI Research Scientist, PhD – Intern

Staff AI Research Scientist

Senior Lead AI Research Scientist – Foundation Models, Agentic AI