Instacart is seeking to evolve and scale its core Catalog and data-intensive systems, while also advancing its Machine Learning serving and serving infrastructure capabilities to shape the future of personalized, real-time ML-driven experiences and drive significant revenue.
Requirements
- Extensive experience in software engineering, with a focus on distributed systems, streaming processing (e.g., Flink), data intensive applications, and particularly, Machine Learning serving and deployment.
- Proven track record of designing, implementing, and scaling large-scale, high-performance systems, including ML serving infrastructure.
- Deep understanding of database technologies, data modeling, data pipelines, and ML model deployment patterns.
- Strong architectural skills and the ability to design and evaluate complex technical solutions across diverse technology domains, including Catalog, Streaming, and Machine Learning.
- Excellent problem-solving and debugging skills, with specific experience in addressing issues related to ML model serving, data quality, and infrastructure stability.
- Experience with cloud platforms and related technologies, including ML serving platforms (e.g., Sagemaker).
- Familiarity with challenges related to ML lifecycle, data flow, and best practices
Responsibilities
- Provide architectural leadership for Catalog, streaming, and data-intensive systems, emphasizing ML serving infrastructure and best practices, and drive the technical roadmap.
- Design, build, and scale reliable, efficient, and adaptable solutions to address changing business and ML needs.
- Lead the development and optimization of ML serving endpoints, ensuring high availability, low latency, robust performance, and implement fail-fast input validations and track metrics using Datadog.
- Centralize ML serving logic and decouple it from product applications to improve debugging, manageability, and system performance.
- Drive and contribute to company-wide transformational initiatives, impacting key business metrics like revenue, personalization, and operational efficiency, and influence the direction of ML infrastructure including real-time inferencing.
- Serve as a subject matter expert for Catalog, streaming, data-intensive, and ML serving technologies, providing guidance and mentorship to engineering and data science teams.
- Identify and implement innovative solutions to optimize system performance, reduce costs, and improve data processing and ML serving latency.
Other
- Strong communication and collaboration skills, with the ability to effectively work across teams, influence stakeholders, and mentor junior engineers.
- Ability to quantify and demonstrate the impact of technical contributions on business results (e.g., revenue, efficiency, cost savings, and ML model performance).
- Experience in a high-growth, fast-paced environment, particularly in the context of scaling ML initiatives.
- Offers may vary based on many factors, such as candidate experience and skills required for the role.
- This role is eligible for a new hire equity grant as well as annual refresh grants.
