Description:

Attentive is seeking a self-motivated, highly driven Senior Software Engineer to join their Machine Learning Operations (MLOps) team.
The role involves enabling Attentive’s Machine Learning (ML) practice to impact the AI product suite through tools for training, inference, and deployment of ML models.
Responsibilities include building and maintaining a foundational ML platform that spans the full ML lifecycle for ML engineers and data scientists.
The team is responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform.
The position offers the opportunity to influence the architectural roadmap for the entire ML organization at Attentive.
Key accomplishments include unlocking offline and real-time access to trillions of data points, managing and optimizing the feature store, and supporting PB scale data operations using various technologies.

Requirements:

Candidates must have 5+ years of experience in Data Engineering or MLOps, with a proven track record of building and maturing pipelines for a PB-scale feature store.
Deep experience with Apache Spark, Spark Streaming, and Ray Data is required, along with the ability to build data pipelines for ML use cases.
A strong understanding of the correlation between data cardinality, query plans, configuration settings, and hardware is essential for optimizing data pipeline performance.
Experience in leading the rollout and operationalization of feature stores such as Tecton or Feast is necessary.
Candidates should understand the key differences between online and offline ML inferences and the critical elements for success in each.

Competitive perks and benefits are offered, including health and wellness programs, equity, and other resources to support employees in bringing their best selves to work.
The US base salary range for this full-time position is $144,840 - $210,000 annually, plus equity and benefits.
Attentive promotes a diverse and inclusive workplace, providing equal employment opportunities and reasonable accommodations for candidates with disabilities.