Machine Learning Engineer

Sunbit builds financial technology for real life. Our technology eases the stress of paying for life's expenses by giving people more options on how and when they pay. Founded in 2016, Sunbit offers a next-generation, no-fee credit card that can be managed through a powerful mobile app, as well as a point-of-sale payment option available at more than 25,000 service locations, including auto dealership service centers, optical practices, dentist offices, veterinary clinics, and specialty healthcare services. Sunbit was included on the 2022 Inc. 5000 list. The financial technology company has also been named a Most Loved Workplace, Best Point of Sale Company, and a Top Fintech Startup by CB Insights. We use cutting-edge innovations in financial technology to bring leading data and features that allow individuals to be qualified instantly, making purchases at the point of sale fast, fair, and accessible for consumers from all walks of life. We create value focused on our core values; we work tirelessly to ensure that Sunbit becomes available to everyone, everywhere.

We invite you to #UnleashYourCuriosity and join our ever-growing R&D organization.

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What You'll Do:

Design, implement, and enhance robust and scalable infrastructure that enables efficient deployment, monitoring, and management of machine learning models in production. In this role, you will bridge the gap between research and production environments, streamline data and feature pipelines, optimize model serving, and ensure governance and reproducibility across our ML lifecycle.

Responsibilities:

• Decouple data prep from model training to accelerate experimentation and deployment
• Build efficient data workflows with versioning, lineage, and optimized resource use (e.g., Snowflake, Dask, Airflow)
• Develop reproducible training pipelines with MLflow, supporting GPU and distributed training
• Automate and standardize model deployment with pre-deployment testing (E2E, dark mode)
• Maintain a model repository with traceability, governance, and consistent metadata
• Monitor model performance, detect drift, and trigger alerts across the ML lifecycle
• Enable model comparison with A/B testing and continuous validation
• Support infrastructure for deploying LLMs, embeddings, and advanced ML use cases
• Manage a unified feature store with history, drift detection, and centralized feature/label tracking
• Establish a single source of truth for features across research and production across research and production

Requirements:

• 3+ years of experience as an MLOps, ML Infrastructure, or Software Engineer in ML-driven environments, preferably with PyTorch.
• Strong proficiency in Python, SQL (leveraging platforms like Snowflake and RDS), and distributed computing frameworks (e.g., Dask, Spark) for processing large-scale data in formats like Parquet.
• Hands-on experience with feature stores, key-value stores like Redis, MLflow (or similar tools), Kubernetes, Docker, cloud infrastructure (AWS, specifically S3 and EC2), and orchestration tools (Airflow).
• Proven ability to build and maintain scalable and version-controlled data pipelines, including real-time streaming with tools like Kafka.
• Experience in designing and deploying robust ML serving infrastructures with CI/CD automation.
• Familiarity with monitoring tools and practices for ML systems, including drift detection and model performance evaluation.

Nice to Have

• Experience with GPU optimization frameworks and distributed training.
• Familiarity with advanced ML deployments, including NLP and embedding models.
• Knowledge of data versioning tools (e.g., DVC) and infrastructure-as-code practices.
• Prior experience implementing structured A/B testing or dark mode deployments for ML models.

Team and Culture:

You will be part of the Data/ML Infrastructure team, which is a central component of the Infrastructure group responsible for cross-company technology initiatives. This specialized team focuses on data infrastructure, AI/ML infrastructure, and all related systems that empower Sunbit's diverse business functions. As a key contributor, you'll have the opportunity to influence company-wide technology decisions, collaborate closely with interdisciplinary teams, and play a crucial role in driving innovative solutions.

Join us in advancing our MLOps platform to enhance reliability, scalability, and efficiency across our machine learning systems.