Model Development

• Design, train, and evaluate ML models for ranking, recommendation, or NLP use cases within the product
• Run structured experiments and A/B tests to validate model impact against product metrics
• Iterate on features, labels, and model architecture based on production feedback

Production & Platform

• Build and maintain training and inference pipelines that run reliably at scale
• Own model deployment, versioning, monitoring, and rollback in production
• Partner with backend engineers to integrate models into existing services with low latency

Ideal Candidate Profile

• 2 to 6 years building and shipping ML models into production systems
• Strong Python skills and hands-on experience with PyTorch or TensorFlow
• Comfortable with the full lifecycle from data pipelines to deployed, monitored models
• Clear written and spoken English and a reliable remote-work setup

Preferred Qualifications

• Experience with MLOps tooling such as MLflow, Airflow, or Kubeflow
• Exposure to large-scale data systems (Spark, feature stores, or streaming pipelines)
• A degree in computer science, statistics, or a related quantitative field

What We Offer

• A core ML seat embedded in a US product company's India engineering hub
• Remote-friendly, pan-India hiring with long-term placement
• Direct exposure to production-scale ML systems and senior engineering leadership
  • One application considered for this and future matched roles