The Staff Software Engineer will develop scalable data systems, mentor engineers, and set engineering standards, focusing on AI and data architecture.
As a Staff Software Engineer, you will take us beyond traditional monolithic SQL engines and batch pipelines. You will build the next generation of distributed data storage and processing systems. You will build systems that can scale indefinitely, and surpass traditional query performance, while making the interfaces for that data simple, expressive, and cleanly abstracted. Your interfaces will support a broad array of data consumers, from our web application, to business analytics, and artificial intelligence.
Primary Duties:
- Identify and develop scalable and performant solutions.
- Work across discipline to shape product strategy and execution.
- Develop the foundations of code architecture and quality.
- Mentor and coach engineers.
- Set and uphold the standard for engineering processes to support high-quality engineering.
Minimum Qualifications:
- BS/BTech (or higher) in Computer Science, Engineering or a related field required.
- 8+ years of production-level experience as an engineer building highly scalable systems.
- 4+ years of experience acting as a trusted technical decision-maker in a team setting, solving for short-term and long-term business value.
- 4+ years of experience working with SQL or other database querying languages on large multi-table data sets.
- Experience architecting, developing, and deploying large-scale distributed systems at scale.
- Experience with cloud technologies, e.g., AWS, Azure, GCP.
- Experience building continuous integration and continuous development (CI/CD) pipelines.
- Strong familiarity with server-side web technologies (eg: Java, Python, Scala, C#, C++, Go).
Preferred KSAs:
- 8+ years of production-level experience as an engineer building highly scalable and reliable infrastructure with at least 3 years focused on AI/ML.
- Experience leading large cross functional data initiatives from ideation to implementation , helping share data platform direction,aligning all the teams and driving broader impact across the organization.
- Experience building low latency data pipelines that serve structured, semi structured and unstructured data for ML and analytics use cases.
- Strong understanding of data modeling, optimization and feature engineering for ML use cases.
- Experience building robust data lineage ,auditing, data quality automation standards for AI/ML related systems.
- In-depth knowledge of database systems and expertise in data processing frameworks (e.g. Spark, Airflow) and strong python and SQL skills.
- Familiarity with database replication, sharding and other techniques for scalability and high availability of databases.
Physical Requirements:
- Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.
Top Skills
Airflow
AWS
Azure
C#
C++
Ci/Cd
GCP
Go
Java
Python
Scala
Spark
SQL
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What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
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