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Sierra

Builds AI agents for customer conversations

Software Engineer - Context Engine

Data EngineeringUpdated on Nov 11
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Candidates should have designed, built, and operated large-scale data systems processing terabytes or petabytes, using technologies such as Spark, Flink, Trino/Presto, and data lakehouse formats such as Iceberg or Hudi.
  • Candidates should have strong backend and distributed-systems fundamentals in a language such as Go, Scala, or Python, and understand trade-offs among storage engines, query plans, and data serialization formats.
  • Candidates should have implemented pragmatic data governance, lineage, and testing, and built cost-aware, highly operable platforms for other engineers.
  • Candidates should know data-modeling practices, including dimensional modeling, star and snowflake schemas, and slowly changing dimensions, and be able to optimize analytical queries for OLAP systems and cloud data warehouses.
  • Candidates should have a portfolio of shipped data products, including analytics dashboards, data-visualization tools, or alerting and exploration systems.
  • Candidates should be skilled full-stack engineers, for example with TypeScript/React and a service layer such as Go, with an eye for performance, API design, and polish.
  • Candidates should be able to turn ambiguous questions into clear, intuitive interfaces and have product sense around information architecture, empty states, and making complex data explainable.
  • Candidates should have practical experience with machine-learning-powered product features such as personalization, recommendations, or classification systems.
  • Candidates should have a strong software engineering background and 4–7+ years of hands-on development experience building and shipping production systems or products.
  • Candidates should have a degree in Computer Science or a related field, or equivalent professional experience.

Responsibilities

  • Join a full-stack data team building systems that make AI agents smarter with every interaction.
  • Architect and build the core data platform to handle data at scale with low latency, including real-time eventing infrastructure, streaming and batch ETL pipelines, and an Iceberg-based data lakehouse.
  • Own systems for interactive OLAP querying, orchestration, and experimentation, ensuring data is trustworthy, fast, and easy for the organization to use.
  • Develop systems for long-term agent memory and reusable Customer Data Platform primitives.
  • Implement optimization loops that enable agents to personalize interactions and measurably improve business outcomes.

Desired Qualifications

  • Deep experience with event-streaming platforms such as Kafka or Kinesis and real-time data processing.
  • Experience with modern data-visualization libraries such as ECharts or D3.js and with building performant, reusable charting components for complex data.
  • Production experience with recommender systems or optimization loops such as multi-armed bandits or ranking.
  • A track record of leading complex technical projects or mentoring other engineers.

About the Company

Sierra

Builds AI agents for customer conversations

Sierra builds AI agents that handle customer conversations for businesses, from answering support questions to guiding purchases and managing account requests. Companies deploy the same agent across voice, chat, email, and messaging channels, with behavior shaped by their brand guidelines and business policies. Its Ghostwriter tool creates and updates agents from plain instructions or existing company materials, then tests changes before teams approve them. The platform connects agents to business systems so they take action, and Sierra charges for completed outcomes rather than the AI tokens consumed. Sierra was founded in 2023 and is headquartered in San Francisco, California, United States.

Company size
1,001-5,000
Company stage
Series E
Total funding
$1.6B
Headquarters
San Francisco, CA
Founded
2023

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