Staff Software Engineer

Summary

Join Domino Data Lab's Model Development Lifecycle Team and contribute to building a cutting-edge platform that simplifies the machine learning journey. In your first year, you will collaborate with customers, introduce a Data Science Catalog, integrate model monitoring, enhance tagging capabilities, and expand LLM hosting capabilities. This role requires 8+ years of software engineering experience, expertise in building scalable systems, API development, performance optimization, testing, and CI/CD. Familiarity with ML model deployment and distributed computing is a plus. Domino values a growth mindset and a diverse team.

Requirements

• 8+ years previously in a software engineering individual contributor role
• Building Scalable Systems : Hands-on experience developing and managing high-performance back-end systems in distributed computing environments
• Collaboration Across Teams : Working closely with cross-functional teams to integrate systems with front-end interfaces and third-party services
• API Development : Designing and implementing secure, scalable APIs (e.g., RESTful APIs, gRPC)
• Performance Optimization : Profiling and optimizing back-end performance, especially in cloud environments or with container technologies like Docker and Kubernetes
• Testing and CI/CD : Using robust testing frameworks (unit, integration, end-to-end) and setting up CI/CD pipelines

Responsibilities

• Collaborate with customers to design solutions for deploying models to platforms like AWS SageMaker and Azure ML
• Introduce a “Data Science Catalog” for discovering and summarizing global data science resources within Domino
• Integrate model monitoring to provide a holistic view of deployment health and performance
• Enhance tagging capabilities across Domino entities to improve discoverability and tracking
• Expand LLM hosting capabilities to address customer needs for scale, performance, and logging

Preferred Qualifications

• ML Model Deployment : Familiarity with model registries, versioning, and lifecycle management tools like MLflow or KubeFlow
• Distributed Computing : Experience with frameworks like Apache Spark, Azure ML, or SageMaker
• Cloud Platforms : Proficiency with cloud providers (AWS, Azure, GCP) and deploying services in these environments

Benefits

• Equity
• Company bonus or sales commissions/bonuses
• 401(k) plan
• Medical, dental, and vision benefits
• Wellness stipends