Job Description
Senior ML Engineer
GoTo
Role Description As a Senior ML Engineer (Data Platform), you would be: - Designing, building, and optimizing scalable batch and near real-time data pipelines using PySpark, SQL, Airflow, and Databricks/AWS. - Developing and maintaining data models for structured and semi-structured datasets. - Working with Delta Lake / Lakehouse architectures for large-scale data processing. - Ensuring data quality, observability, and reliability across pipelines. - Participating in architecture discussions on scalability, performance, and cost optimization. - Designing and building end-to-end ML systems in production for SaaS use cases like churn, recommendations, and anomaly detection. - Owning the ML lifecycle - Feature engineering pipelines, Model training and evaluation, Batch / real-time inference, Monitoring and retraining. - Building and maintaining MLOps pipelines, including model versioning, deployment, monitoring, and rollback. - Integrating ML models into data platforms and downstream applications with reliability and observability. - Partnering with Product, Marketing, Sales, and Platform teams to translate business problems into scalable ML solutions. - Mentoring engineers and contributing to data and ML engineering best practices. Qualifications - 5+ years of experience in data engineering or backend engineering, with ownership of production data systems. - Strong hands-on experience in PySpark and SQL for distributed data processing. - Experience with Databricks and/or AWS data stack (EMR, EKS, S3, etc.). - Deep understanding of ETL/ELT design, data modeling, and performance optimization. - Hands-on experience building and deploying ML systems in production (e.g., churn, recommendations, anomaly detection — not just POCs). - Experience across the ML lifecycle: Feature engineering pipelines, Model training and evaluation, Batch / real-time inference, Monitoring and retraining. - Practical exposure to MLOps practices, including model versioning, deployment pipelines, and performance monitoring. - Strong debugging and problem-solving skills in production environments. - The position requires a combination of GenAI/LLM knowledge along with solid expertise in data engineering and traditional ML systems. - Exposure limited to LLM frameworks, prototypes, or experimentation without production-grade system experience may not meet the scope of this role. Requirements - Nice-to-have: 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). Benefits - Comprehensive health benefits, life and disability insurance, and fertility and family-forming support program. - Generous paid time off, paid holidays, volunteer time off, and quarterly self-care days and no meeting days. - Tuition and reading reimbursement programs to support your continuous learning and professional growth. - Thrive Global Wellness Program, confidential Employee Assistance Program (EAP), as well as One to One Wellness Coaching. - Employee programs—including Employee Resource Groups (ERGs), GoTo Gives, and our charitable matching program—to amplify your connection and impact. - GoTo performance bonus program to celebrate your impact and contributions. - Monthly remote work stipend to support your home office expenses.
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