Staff ML Scientist

You will combine deep technical expertise with ownership, velocity, and influence. You will drive the scientific roadmap at scale, ensuring models are aligned, explainable, and robust, and you’ll collaborate with world‑class experts while influencing product strategy in a high‑stakes domain. The position offers access to top‑tier compute, tools, and datasets, and a collaborative, innovative culture focused on shaping the future of global payments.

Responsibilities:

• Extract insights from large-scale Visa datasets across business lines to define high-value, fit-for-purpose problems beyond payments - driving clarity and framing business impact with precision.

• Fine-tuning open-weight LLMs to reduce cost – bring deep expertise in parameter-efficient fine-tuning (LoRA/QLoRA) and distillation to achieve near frontier performance at significantly lower inference and training cost.

• Classifier as an LLM model - frame classification problems (e.g., fraud signals, intent detection) as generative tasks using LLMs, enabling better generalization and faster iteration vs. traditional pipelines.

• Lead other ML PoCs - risk scoring of stablecoins, intent misalignment, and merchant discoverability for agentic commerce. Rapidly prototype and validate new problem spaces with lightweight experimentation frameworks to identify high-ROI opportunities for scaling.

• Rapidly design, experiment, and deploy SOTA ML/AI models, owning the full lifecycle and ensuring solutions are scalable, measurable, and aligned with strategic goals.

• Lead future-forward AI/ML research, proactively identifying emerging techniques, publishing influential work, and filing patents to amplify Visa’s innovation footprint.

• Integrate models into production with engineering and product teams, building robust, compliant pipelines with an eye on velocity, reliability, and business outcomes.

• Mentor scientists using Generative AI tools, cultivating technical depth, research excellence, and a culture of rigor and collaboration

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. 

Basic Qualifications:

• 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.

Preferred Qualifications:

• 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
• Strong fundamentals in machine learning, deep learning, and optimization and solid understanding of the math behind them. You should be able to derive algorithms from first principles and understand their limitations. PhD/MS in computer science, machine learning or related field preferred.
• Proven track record (6+ years) of building AI / ML models which can handle scale (think model size, latency, architecture, etc). You’ve shipped models that matter - ideally in production environments with real users and measurable impact. You have deep technical and domain expertise in at least two domains where you are able to connect different ML techniques (e.g. contrastive learning, domain adaptation, etc.) to appropriate business problems.
• Fluency in code. You write clean, efficient Python, and you’re comfortable navigating large codebases. You treat code as a medium for clarity and experimentation. You have experience in PySpark, PyTorch, tensor flow, as well as GPU based compute environments.
• Research mindset with product intuition. You know when to read papers and when to write them. You can balance novelty with practicality and know how to translate ideas into working systems. You are deeply technical covering both ML as well as deep learning fundamentals spanning design, pre-training, fine-tuning, and evaluation.
• Ownership and velocity. You take initiative, move fast, and iterate. You don’t wait for permission, you define the problem, build the solution, and drive it to completion.
• Communication clarity. You can explain complex ideas simply, whether in a design doc, a pull request, or a hallway conversation. You write well and think clearly.