Senior Machine Learning Engineer

Requirements

• As a Senior ML Engineer, your background will look like
• 5+ years of experience in data engineering, ML engineering, or backend engineering with exposure to production systems
• Hands-on experience with PySpark and SQL for large-scale data processing
• Experience working with Databricks and/or AWS data stack (EMR, EKS, S3, Glue, etc.)
• Good practical understanding of data pipelines, ETL/ELT workflows, and distributed processing systems
• Hands-on experience building and deploying ML systems for real business problems (not just POCs or experimentation)
• Exposure across the ML lifecycle including feature engineering, training, inference, monitoring, and retraining workflows
• Practical exposure to MLOps concepts such as deployment pipelines, model versioning, monitoring, and production debugging
• Strong debugging and problem-solving skills in production environments
• Ability to work across data pipelines, ML systems, and production workflows with strong ownership.
• Experience building feature pipelines or feature stores
• Exposure to streaming systems (Kafka, Spark Streaming, Kinesis)
• Experience with CI/CD pipelines for data and ML workflows
• Familiarity with data governance frameworks (e.g., Unity Catalog)
• Exposure to AI/LLM-based systems (RAG, etc.) as an extension (not primary focus)
• Core Hiring Principle
• We are NOT hiring a pure Data Engineer or pure Data Scientist
• We are hiring someone who can build and own ML systems in production
• What Success Looks Like
• Building reliable ML pipelines and systems
• Operating ML workflows in production
• Collaborating across data, product, and engineering teams
• Taking practical engineering decisions with strong ownership.

Benefits

Additional Information

Where you'll work: Bangalore, KA, IN Engineering at GoTo We're trailblazers in remote work technology-building powerful, flexible solutions that empower everyone to live their best life, both at work and beyond. With us, you'll have the opportunity to chart new paths and help redefine how the world works. For us, AI isn't just a buzzword; it's a tool we use to deliver real, practical value to our customers and teams. We focus on solving meaningful problems, not just adding features for the sake of using AI. Here, growth takes many forms: you can expand your skills, take on new challenges, lead initiatives, and explore creative ideas. Join a GoTo product team and play a key role in transforming the workplace for millions of users worldwide-your work will truly make a difference. Where you'll work: Remote / Bangalore Engineering at GoTo We're trailblazers in remote work technology-building powerful, flexible solutions that empower everyone to live their best life, both at work and beyond. With us, you'll have the opportunity to chart new paths and help redefine how the world works. For us, AI isn't just a buzzword; it's a tool we use to deliver real, practical value to our customers and teams. We focus on solving meaningful problems, not just adding features for the sake of using AI. Here, growth takes many forms: you can expand your skills, take on new challenges, lead initiatives, and explore creative ideas. Join a GoTo product team and play a key role in transforming the workplace for millions of users worldwide-your work will truly make a difference. Your Day to Day As a Senior ML Engineer - you would be: Build and maintain scalable batch and near real-time data pipelines using PySpark, SQL, Airflow, Databricks, and AWS services Work with structured and semi-structured datasets using modern lakehouse/data platform architectures Ensure data quality, reliability, observability, and operational stability across pipelines Design and operate production ML systems for use cases such as recommendations, churn prediction, anomaly detection, and usage intelligence Work across the ML lifecycle including: feature engineering model training and evaluation batch / real-time inference monitoring and retraining Build and maintain practical MLOps workflows including deployment, model versioning, monitoring, and rollback strategies Partner with Product, Marketing, Sales, Platform, and Engineering teams to translate business problems into scalable ML solutions Contribute to architecture discussions around scalability, performance, reliability, and cost optimization Mentor engineers and contribute to engineering best practices

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