Level

Visa

Sr. ML Scientist (4-6 years' experience, Build Transformer-based models, Inference Engineering)

Visa is hiring a Sr. ML Scientist (4-6 years' experience, Build Transformer-based models, Inference Engineering) in Bengaluru, India. Level rates it ; you can apply on Visa.

Level's read on this job

  • Level rates this role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions PyTorch and fine tuning among its tools and skills.
  • Level classes it as a mid-level role.
  • Visa has 849 other open roles on Level.
  • Posted in the last few days.

AI in this role

pytorch
fine-tuning

About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.

At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.

Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.

Job Description

Overview:

We're building AI that protects and powers global commerce and money movement. As a Senior ML Scientist, you'll work in a small world-class team of scientists and inference engineers to transform breakthrough research into real-world products, using machine learning, foundation models, and intelligent decision systems to solve high-impact challenges at unprecedented scale.


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.


Key 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.
  • 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.

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

Qualifications

  • 4+ years of relevant work experience and a Bachelors degree, OR with an Advanced Degree in computer science, machine learning or related field preferred
  • Strong fundamentals in machine learning: Deep learning, RL, and optimization and solid understanding of the math behind them. You should be able to derive algorithms from first principles and understand their limitations. 
  • Proven track record (3+ years) of building AI / ML models which can handle scale (think model architecture, size, latency, architecture, etc). You’ve shipped models that matter in production environments with real users and measurable impact. 
  • You have experience in working in multiple domains, and have been able to connect different techniques to appropriate business problems, in the past. 
  • 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.
  • 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.
  • 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.

Visa is an EEO Employer

Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.

How we rate this

Sr. ML Scientist (4-6 years' experience, Build Transformer-based models, Inference Engineering) at Visa rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningPyTorch

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. How would you decide a model or AI system is ready to ship?
  4. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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