Software Engineer, Context Engine

Sierra•San Francisco, CA

1d•$230,000 - $390,000

About The Position

Join Sierra’s full-stack data team to build systems that make customer-facing AI agents smarter with every interaction. The team develops real-time data pipelines, analytics products, and personalization capabilities that turn conversations into business outcomes. The role spans data foundations and memory and personalization. In data foundations, you will build the core platform for low-latency, large-scale data processing, including real-time eventing, streaming and batch ETL, and an Iceberg-based data lakehouse. You will own interactive OLAP querying, orchestration, and experimentation systems, with a focus on trustworthy, fast, usable data. In memory and personalization, you will develop long-term agent memory, reusable Customer Data Platform primitives, and optimization loops that personalize interactions and improve business outcomes. The role welcomes candidates with depth in either platform engineering or product engineering. Platform engineers build and operate large-scale data systems and create cost-aware, operable platforms with sound governance, lineage, testing, and data modeling. Product engineers build shipped data products and full-stack interfaces, turning ambiguous questions into clear and polished experiences, and may work on ML-powered product features. The position calls for strong software engineering experience, an interest in frontier AI products, and initiative in a high-autonomy environment.

Requirements

Strong software engineering background with 4–7+ years of hands-on development experience building and shipping production systems or products.
Degree in Computer Science or a related field, or equivalent professional experience.
Passion for working on frontier AI products.
High agency and a bias to action in a high-autonomy environment.
For platform engineering: experience designing, building, and operating 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.
For platform engineering: strong backend and distributed systems fundamentals in a language such as Go, Scala, or Python, and understanding of storage engine, query plan, and data serialization trade-offs.
For platform engineering: experience implementing pragmatic data governance, lineage, and testing, and building cost-aware, operable platforms for other engineers.
For platform engineering: knowledge of data modeling practices, including dimensional modeling, star/snowflake schemas, and slowly changing dimensions, and ability to optimize analytical query patterns for OLAP systems and cloud data warehouses.
For product engineering: a portfolio of shipped data products, such as analytics dashboards, data visualization tools, or alerting and exploration systems.
For product engineering: full-stack engineering skills, for example TypeScript/React and a service layer such as Go, with attention to performance, API design, and polish.
For product engineering: ability to turn ambiguous questions into intuitive interfaces, with product sense for information architecture, empty states, and making complex data explainable.
For product engineering: practical experience with ML-powered product features such as personalization, recommendations, or classification systems.

Nice To Haves

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 building performant, reusable charting components for complex data.
Production experience with recommender systems or optimization loops such as multi-armed bandits or ranking.
Track record of leading complex technical projects or mentoring other engineers.

Responsibilities

Architect and build core data platform systems capable of handling data at scale with low latency.
Build real-time eventing infrastructure, streaming and batch ETL pipelines, and an Iceberg-based data lakehouse.
Own systems for interactive OLAP querying, orchestration, and experimentation.
Ensure data systems are trustworthy, fast, and easy for the organization to use.
Develop long-term agent memory systems and reusable Customer Data Platform primitives.
Implement optimization loops that personalize agent interactions and improve business outcomes.
Build and ship production data systems or products, such as data platforms, analytics dashboards, visualization tools, or alerting and exploration systems.

Benefits

Flexible (unlimited) paid time off.
Medical, dental, and vision benefits for employees and their families.
Life insurance and disability benefits.
Retirement plan dependent on country of employment.
Parental leave.
Fertility and family-building benefits through Carrot.
Lunch, snacks, and coffee.
Discretionary benefit stipend.
Free alphorn lessons.
Eligible full-time employees can participate in Sierra’s equity plans, subject to applicable plan terms and policies.