Udio — Senior Backend Engineer, Data Modeling and Ingestion Platform

job posting·professional role·active

Official active posting for Senior Backend Engineer, Data Modeling and Ingestion Platform at Udio, located in New York.

Recorded facts

Official site	https://job-boards.greenhouse.io/udio/jobs/4988140008 ↗
Geography	United States
job board posting id	4988140008
team	unknown
location	New York
seniority	senior
department	Technology
exact title	Senior Backend Engineer, Data Modeling and Ingestion Platform
role family	data_and_analytics
closing date	unknown
compensation	raw: $180,000 - $220,000. · period: year · maximum: 220000 · minimum: 180000 · currency: USD · disclosed: true · parse state: parsed
posting date	2025-11-13
employer name	Udio
posting status	active
workplace type	on-site
employment type	unknown
required skills	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.
selection basis	official_current_board_freshness_or_domain_critical_role
employer subtype	generative_music_company
preferred skills	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 creativity, algorithmic thinking, and engineering excellence.; You'll join a small, fast-moving team where your decisions shape the direction of our data and research capabilities.
responsibilities	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.; 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.
work authorization	unknown
education requirements	unknown