2027 Quantitative Analytics Program – Applied Computational Intelligence (ACI Masters) – Early Careers
About The Position
Wells Fargo is seeking talent to join the 2027 Quantitative Analytics Program ACI (Masters). This 12-month development program combines hands-on project experience, mentorship, technical training, and exposure to senior leaders. Through two six-month rotations, you'll work alongside experienced quantitative professionals, helping develop and evaluate innovative solutions that support business strategy, risk management, and customer experience across Wells Fargo. You'll be expected to bring fresh perspectives, explore innovative approaches, and contribute to solutions that support Wells Fargo's strategic priorities. Along the way, you'll develop not only your technical capabilities but also the business acumen and leadership skills needed to succeed in a highly collaborative environment. Upon completion of the program, you'll transition into a full-time role aligned with your skills, interests, program experience, and business needs. As part of the Applied Computational Intelligence track, you'll work on innovative projects that leverage emerging AI technologies and advanced modeling techniques. Example projects may include developing AI-powered advisors and decision support systems, building Generative AI assistants and intelligent agents, designing and deploying agentic AI and multi-agent systems, creating enterprise knowledge intelligence platforms, advancing the state of enterprise AI through model training, and deploying scalable Generative AI and machine learning solutions. You will also apply statistical and quantitative techniques to validate model design, calibration, and implementation.
Requirements
- 6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- Strong programming experience with tools such as Python, R, SQL, Java, Spark, or similar technologies
- Hands-on experience developing machine learning and AI solutions in research, academic, or industry environments
- Knowledge and Experience In: Large Language Models & Model Training
- Experience with supervised fine-tuning (SFT) and post-training methodologies including RLHF, RLAIF, PPO, DPO, and GRPO
- Training and deploying models in cloud environments, including GCP
- Agentic AI
- Multi-agent architectures and orchestration frameworks like LangChain, LangGraph, Google's ADK, CrewAI
- Retrieval-Augmented Generation (RAG) applications and intelligent agent deployment
- AI Infrastructure & Optimization
- Distributed GPU training
- Efficient model tuning approaches such as LoRA and PEFT
- Strong quantitative and analytical skills, with the ability to apply data analysis, modeling, visualization, statistics, research, and generative AI to generate insights, adapt quickly, and support innovative solutions.
- Ability to execute with urgency, apply data and software engineering skills to design, develop, and deliver scalable solutions, and drive operational excellence with strong data management and an enterprise mindset.
- Strong communication skills, with the ability to foster an inclusive environment and actively seek, apply, and respond to feedback in collaborative analytical settings
- Strong business acumen with a commitment to providing excellent service and supporting data-informed business outcomes.
- Ability to act with integrity, support risk assessments, and apply risk controls to help manage risk in a disciplined, data-driven environment.
Nice To Haves
- Currently pursuing a Masters degree in Computer Science, Machine Learning, Artificial Intelligence, Engineering or related quantitative field or related quantitative field, with an expected graduation date between December 2026 – June 2027.
Responsibilities
- Develop and deploy AI and machine learning solutions across generative AI, agentic systems, and traditional machine learning applications
- Design and build LLM-powered agents and multi-agent systems capable of planning, reasoning, task orchestration, and human-in-the-loop collaboration
- Monitor production models and AI systems, evaluating performance, stability, and model drift through testing and analytics frameworks
- Apply advanced quantitative techniques to solve real-world business problems
- Collaborate with cross-functional teams, technical experts, and business leaders across the organization
- Gain exposure to enterprise-scale AI development, governance, and risk management practices
- Design, train, fine-tune, and evaluate transformer-based, foundation, and small language models (SLMs)
- Apply AI, machine learning, and generative AI techniques to solve complex business problems
- Build and optimize scalable model training and deployment pipelines
- Leverage distributed computing and advanced training techniques to improve model performance and efficiency
- Enhance model performance through distillation, quantization, and pruning
- Optimize inference speed, latency, throughout, and cost for production of AI systems
Benefits
- Mentorship
- Technical training
- Exposure to senior leaders
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
