Senior Backend Engineer, Data Modeling and Ingestion Platform

We are looking for a Senior Backend Engineer to lead the unification of large, highly rich, and heterogeneous datasets sourced from a wide range of external providers. These datasets are used to power our generative audio models. 

Your work will create the foundational dataset that powers our research by building robust, scalable systems for linking, deduplicating, reconciling, and enriching data at massive scale. This role centers on high-impact bulk ingestion and advanced data linkage. You will design the logic, algorithms, and strategies that transform many independent datasets into a unified, high-quality canonical asset used throughout the company.

You will collaborate closely with ML researchers and product teams, working with tools such as BigQuery, Dataflow/Beam, TFRecords, and—where beneficial—distributed systems frameworks like Ray. Familiarity with ML workflows using JAX or multihost training is a plus, as the datasets you produce will directly support that ecosystem.

• →Build high-throughput bulk ingestion workflows to integrate datasets from multiple external providers. 
• →Design and implement scalable entity-resolution solutions, including record linking, deduplication, clustering, and conflict arbitration. 
• →Create and refine matching logic, decision rules, and similarity functions to align datasets with high accuracy and strong coverage. 
• →Define and track data quality indicators, such as overlap metrics, match precision/recall, duplicate rates, and completeness. 
• →Prepare training-ready datasets in formats such as TFRecords, and structure data to meet ML research requirements. 
• →Develop processing components using Dataflow (Beam) and manage large analytical workloads in BigQuery. 
• →Leverage frameworks like Ray to accelerate large-scale experiments, feature extraction, and research-oriented data preparation. 
• →Collaborate with ML researchers to anticipate downstream requirements and evolve linkage strategies as new sources and use cases emerge. 

• Experience working with large, heterogeneous datasets from multiple providers or domains. 
• Strong background in entity resolution, deduplication, data unification, or related large-scale data integration techniques. 
• Proficiency in Python, with an emphasis on efficient, scalable data processing. 
• Experience with BigQuery, Google Dataflow/Apache Beam, or similar batch-processing frameworks. 
• Familiarity with data validation, normalization, reconciliation, and building consistent views across diverse data sources. 
• Ability to craft well-structured matching and decision strategies that balance accuracy, completeness, and computational efficiency. 
• Comfortable iterating quickly on pragmatic solutions, balancing correctness with time-to-delivery. 
• Clear communication skills and the ability to collaborate closely with ML and research teams. 

• Knowledge of architecting Google Cloud Platform systems at scale
• Experience with distributed compute frameworks such as Ray, Spark, or Flink. 
• Understanding of JAX-based ML pipelines, multihost training setups, or large-scale data preparation for accelerator-backed workflows. 
• Familiarity with TFRecords or other high-volume training data formats. 
• Exposure to ranking, clustering, or statistical similarity modeling. 
• Experience with Go, NextJS, and/or React Native to contribute to full-stack development