Shape the future of intelligent product experiences as a Staff Software Engineer with deep expertise in Ruby and distributed systems. In this role, you'll lead architectural decisions across multiple teams, ensuring robust, scalable implementations of AI-driven features within a production environment. Your work will directly influence how machine learning capabilities are integrated into customer-facing products, combining technical rigor with real-world usability.
What You’ll Do
- Guide technical strategy across engineering teams, defining system architecture and advancing engineering practices for large-scale, cloud-based applications.
- Partner closely with ML Scientists and Engineers to operationalize machine learning models, focusing on reliability, performance, and seamless integration into product workflows.
- Design and evolve complex, cross-service systems—from initial concept through deployment and iteration—using real user data to inform improvements.
- Drive initiatives that enhance system efficiency, scalability, and developer velocity, balancing technical debt with delivery pace.
- Collaborate with Product, Design, and leadership to align engineering roadmaps with business objectives and customer needs.
- Mentor engineers across the organization through hands-on code reviews, design discussions, and knowledge sharing to raise overall technical standards.
- Act as a technical liaison between product development and machine learning teams, enabling practical trade-offs that accelerate delivery without sacrificing quality.
What We’re Looking For
- 10+ years of experience building and maintaining internet-scale applications, with a history of guiding technical direction across teams.
- Strong command of Ruby; familiarity with Python is highly valued.
- Proven background in distributed systems, REST APIs, event-driven messaging (e.g., Kafka), and cloud platforms like AWS.
- Extensive experience with containerization (Docker, Kubernetes) and modern CI/CD pipelines.
- Ability to lead cross-functional technical projects from idea to production, with a focus on shipping working solutions.
- Comfort working alongside data science teams—you don’t need to build models, but you understand model serving, LLM integration, and how to productize ML features reliably.
- Strong SQL and data infrastructure skills, including data pipeline design and performance optimization.
- Demonstrated success mentoring engineers and improving team-wide technical excellence.
- Skill in navigating uncertainty, making informed trade-offs, and providing clarity when priorities shift.
Nice to Have
- Experience with Snowflake and dbt for data modeling and analytics workflows.
- Hands-on work integrating large language models into production systems, including prompt engineering, evaluation, monitoring, and cost control.
- Familiarity with ML pipeline tools such as Metaflow or similar frameworks.
- Background in metrics-driven development: A/B testing, feature flagging, and incremental rollouts.
- Experience building products where AI is embedded within a broader user experience.
Technology Environment
Ruby, Python, AWS, Metaflow, S3, RDS MySQL, Snowflake, dbt, Kubernetes, Docker, Kafka, and LLM technologies.
Work Model
This is a hybrid role, combining remote flexibility with periodic in-office collaboration. You’ll be expected to attend the local office several days per week, with the exact schedule set by your team’s leadership.
