As a Staff Software Engineer, you will play a pivotal role in shaping the technical direction of systems that power intelligent, customer-facing features at scale. Your work will directly influence how machine learning capabilities are integrated into production-ready experiences, balancing innovation with reliability.
What You'll Do
- Guide architectural decisions across multiple engineering teams, ensuring systems are robust, maintainable, and aligned with long-term goals.
- Collaborate closely with machine learning engineers and scientists to productionize ML features, focusing on seamless integration and real-world usability.
- Design and own complex, cross-service systems—from concept through deployment—and refine them based on operational feedback.
- Champion initiatives that improve performance, scalability, cost efficiency, and developer velocity across the engineering organization.
- Partner with Product Management and Design to align technical strategy with product objectives and customer needs.
- Support the growth of engineers through hands-on mentorship, code and design reviews, and by modeling strong engineering practices.
- Serve as a technical liaison between product engineering and data science, enabling effective collaboration and pragmatic decision-making.
Requirements
- Minimum of 10 years building and maintaining large-scale, public-facing distributed systems, with proven leadership in technical architecture.
- Deep expertise in Ruby; familiarity with Python is strongly preferred.
- Extensive experience with cloud platforms (AWS), containerization (Docker, Kubernetes), and modern CI/CD pipelines.
- Solid understanding of RESTful APIs, event-driven systems (e.g., Kafka), and distributed data storage solutions.
- Experience shipping cross-team initiatives from idea to production, with a focus on execution over theory.
- Ability to work effectively with ML teams—understanding model serving, LLM integration patterns, and the operational needs of ML-powered features.
- Strong command of SQL and data infrastructure, including data pipelines, query optimization, and analytics workflows.
- Skilled at making forward-looking technical decisions in ambiguous environments and adapting to shifting priorities.
- History of mentoring engineers and raising technical standards across teams.
Benefits
- Hybrid work model with a balance of in-office collaboration and remote flexibility. The specific schedule is set by the hiring manager.
