Job Opportunities API

About the Role 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. What You'll Do 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.  What We're Looking For  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.   Nice to Have 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 Why Join Us You will design the  core dataset  that underpins our research, product development, and generative audio models.  You'll work on large-scale data challenges that require cr