Senior Software Engineer(ai/ml), Trust

Airbnb Airbnb · Consumer · Bangalore, India · Software Engineering

Senior Software Engineer (AI/ML) on the Trust team at Airbnb, focusing on building and improving ML systems for fraud detection, user screening, and platform safety. Responsibilities include hands-on productionizing and operating ML solutions at scale, collaborating with cross-functional teams, and contributing to the technical vision for trust defenses.

What you'd actually do

  1. Work with large scale structured and unstructured data, build and continuously improve novel ML systems, product integrations, and performance optimizations for Airbnb product, business and operational use cases.
  2. Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for AI/ML models, drive engineering decisions, and quantify impact.
  3. Work closely with other trust defense and platform teams to tackle the changing landscape of fraud attacks.
  4. Hands-on productionize, and operate AI/ML solution and pipelines at scale, including both batch and real-time use cases.
  5. Lead, mentor, challenge and grow enthusiastic, collaborative AI/ML culture within the organization

Skills

Required

  • Python
  • Java
  • DSA
  • data engineering foundations
  • ML best practices
  • Tensorflow
  • PyTorch
  • Kubernetes
  • Spark
  • orchestration (Airflow/Kubeflow)
  • streaming/processing (Kafka/Spark/Ray)
  • data warehouse (eg. Hive)
  • building observability for AI systems
  • automated alerting
  • dashboards
  • SLO management
  • test driven development
  • A/B testing
  • incremental delivery and deployment

Nice to have

  • applied Machine Learning
  • building and productionizing Machine Learning models
  • Trust and Risk domain

What the JD emphasized

  • Must have experience working in large tech product companies solving real world problems.

Other signals

  • productionize ML systems
  • build and continuously improve novel ML systems
  • operate AI/ML solution and pipelines at scale