Staff Machine Learning Engineer

PayPal PayPal · Fintech · Bengaluru, KA, IN · Machine Learning Engineering

Staff Machine Learning Engineer at PayPal in Bengaluru, India, focused on developing and deploying advanced ML models, particularly in Generative AI and Agentic AI, with a strong emphasis on production readiness, fine-tuning, and evaluation within the fintech domain.

What you'd actually do

  1. Lead the development and optimization of advanced machine learning models.
  2. Oversee the preprocessing and analysis of large datasets.
  3. Deploy and maintain ML solutions in production environments.
  4. Collaborate with cross-functional teams to integrate ML models into products and services.
  5. Monitor and evaluate the performance of deployed models, making necessary adjustments.

Skills

Required

  • 5+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
  • Extensive experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
  • Knowledge of Credit, Payments or any other financial product or services domain
  • Deep expertise in foundation model development using transformer architectures, including pretraining strategies, model scaling, and optimization techniques
  • Proficient in LLM fine-tuning methodologies including supervised fine-tuning (SFT), RLHF, REFT, DPO, and parameter-efficient methods such as LoRA and QLoRA
  • Strong experience designing and implementing LLM guardrails, including safety filters, output validation, toxicity detection, and policy enforcement frameworks
  • Deep experience in building large scale recommender systems for product/content recommendation
  • Solid understanding of foundation models for recommendation systems, including architectures such as Transformers4Rec
  • Hands-on experience building production-grade Agentic AI applications, including multi-agent orchestration, tool use, memory systems, and autonomous reasoning pipelines
  • Ability to pick up new agentic AI frameworks
  • Ability to provide technical direction and mentorship to cross-functional data science and engineering teams, driving architectural decisions and best practices across the AI stack
  • Effective at translating business problems to AI solutions that can scale
  • Strong AI measurement skills with experience designing evaluation frameworks, benchmarks, and metrics for model quality, fairness, robustness, and business impact
  • Ability to define and track experimentation rigor — A/B testing, offline vs. online evaluation, and model monitoring in pro

Nice to have

  • Experience with RL based recommenders such as Multi-armed bandits is an added plus

What the JD emphasized

  • Deep expertise in foundation model development
  • Proficient in LLM fine-tuning methodologies
  • Hands-on experience building production-grade Agentic AI applications
  • Strong AI measurement skills

Other signals

  • Deploy and maintain ML solutions in production environments
  • Deep expertise in foundation model development
  • Proficient in LLM fine-tuning methodologies
  • Hands-on experience building production-grade Agentic AI applications
  • Strong AI measurement skills