Senior Staff Machine Learning Engineer - Platform

🔹 Role Snapshot

• Title: Senior Staff Machine Learning Engineer – Platform
• Location: Remote (Bengaluru, India)
• Level: Senior Staff (tech lead / principal-equivalent)
• Department: Platform Product Group → Machine Learning
• Employment Type: Full-time
• Job ID: GML07IN

🔹 Mission Context

Coinbase is aiming to build the infrastructure for the open financial system, and ML plays a critical role in:

• Fraud detection across blockchain + off-chain data
• Risk assessment and intelligence-driven user experience
• Improving internal developer efficiency by democratizing ML usage across product teams

This role is a technical linchpin—you’re not just training models, you're building the ML platform infrastructure to enable others, influence ML adoption across the org, and push innovation in key crypto use cases.

🔹 Core Responsibilities

• Develop end-to-end ML systems including data pipelines, feature engineering, model training/deployment, and monitoring
• Apply advanced techniques like:
• Graph Neural Networks (GNNs) – for modeling blockchain interactions
• Large Language Models (LLMs) – for chatbots, customer experience, knowledge systems
• Deep Learning / Gradient Boosting / Logistic Regression – for fraud, classification, recommendations
• Build reusable onboarding tools and ML codelabs/documentation to scale access
• Ensure production-grade reliability and model governance
• Drive ML strategy, experimentation, and deployment velocity across engineering teams

🔹 Required Qualifications

• 10+ years of industry experience in software and ML engineering
• Solid backend engineering experience for data pipelines, distributed systems, and ML platforms
• Demonstrated hands-on experience with at least one ML model type (LLM, GNN, DL, XGBoost, etc.)
• Strong communication and cross-functional collaboration skills
• Focus on building for scale and robustness in production

🔹 Nice-to-Have Skills

• Advanced degree (MS, PhD) in CS, AI/ML, or related fields
• Familiarity with the following tools and platforms:
• Apache Spark, Airflow, Flink, Kafka, Kinesis, Hive, Hadoop
• Snowflake for data warehousing
• Python (heavy ML language)
• Experience with:
• Model interpretability, fairness, explainability
• Crypto/Blockchain data sources
• Data visualization tools (e.g., Plotly, Dash, Looker)

🔹 Why This Role Stands Out

• Scope + Seniority: You're building ML tools used by all engineers—you’re not just part of a product team, you’re influencing org-wide development.
• Crypto-Native ML Applications: Real-world AI/ML usage in onchain behavior modeling, fraud, DeFi pattern detection, and chain analytics.
• Technical Complexity: Low-latency, high-volume data from blockchains is hard—especially when fused with offchain transactional and user behavior data.
• Visionary but Grounded: LLMs and GNNs are not “for research” here—they’re expected to power production use cases.