Suno Senior/Staff Software Engineer at Suno building and scaling data infrastructure for personalized music recommendations. Design streaming pipelines and infrastructure supporting ML model training and real-time personalization.
Responsibilities
Suno is growing fast, and we’re hiring Staff and Senior Software Engineers to build and scale the foundational systems that power our data platform and personalized recommendations. You’ll design and operate infrastructure that ingests, processes, and serves PBs of data, helping connect listeners with music they love while supporting analytics, experimentation, and machine learning across the company.
This is a high-impact, senior role at the intersection of Data Infrastructure and ML. You’ll partner closely with Engineering, Data, and ML teams to build the pipelines, feature infrastructure, and low-latency data services that power recommendations and music discovery. Your work will help turn user interactions and content signals into reliable, fresh data for model training and real-time personalization.
As Suno grows, you’ll help define the long-term architecture of our data platform, balancing scalability, reliability, latency, and cost. You’ll build systems that are observable and easy to extend, enabling teams to experiment quickly and improve how people discover and engage with music. You’ll own major systems end to end, from design through production operation, and drive architecture and technical direction across teams.
Check out the Suno version of this role here: https://suno.com/s/yt9eiMi9aj0rmGX6
Build and scale the data infrastructure behind personalized recommendations and music discovery, working closely with ML and product engineers.
Design streaming pipelines that transform user interactions and content signals into fresh features for recommendation models.
Develop infrastructure for computing, storing, and serving features across offline model training and online inference, with attention to consistency, reliability, and latency.
Enable recommendation experimentation through reliable impression and interaction logging, supporting model evaluation and measurement of recommendation quality.
Own the architecture and reliability of core streaming and batch infrastructure, using technologies such as Kinesis, Firehose, Lambda, S3, Flink, and Glue.
Build and maintain data workflows and orchestration using Dagster, ensuring pipelines are modular, testable, and observable.
Partner with backend and product engineers to define event schemas, data contracts, and ingestion patterns from production systems.
Improve the reliability and observability of data pipelines through monitoring, alerting, lineage tracking, and automated recovery mechanisms.
Qualification
Strong programming skillsUnderstanding of feature computationExperience designing reliableStrong understanding of data modelingAbility to collaborate closely with MLA strong sense of ownershipExcitement about musicExperience with DagsterExperience with dbt for transformationsAdditional Notes
Required
7+ years of relevant experience in software engineering, data infrastructure, or distributed systems.
Strong programming skills, preferably in Python, and experience building and operating production-grade distributed systems.
Deep experience building and operating real-time streaming and batch processing systems.
Experience building data or serving infrastructure for recommendations, personalization, search, or similar ML-powered product experiences.
Understanding of feature computation, online/offline data consistency, and the latency and freshness requirements of production ML systems.
Experience with streaming and cloud data infrastructure, such as Flink, Kinesis, Kafka, or equivalent technologies.
Experience designing reliable, observable systems, including monitoring, logging, alerting, and recovery.
Strong understanding of data modeling, schema evolution, and data quality.
Ability to collaborate closely with ML, data, and product engineering teams, make pragmatic technical decisions, and deliver in ambiguous environments.
A strong sense of ownership, with a track record of delivering scalable infrastructure and improving engineering practices.
Excitement about music, creativity, and AI, and the infrastructure that helps people create and discover music.
Preferred
Experience with AWS services such as Kinesis, Firehose, Lambda, S3, and Glue.
Experience with Snowflake or a similar cloud data warehouse, including performance and cost optimization.
Experience with Dagster, Airflow, or similar orchestration frameworks.
Experience with dbt for transformations, testing, and modular data modeling.
Experience with Terraform or similar infrastructure-as-code tools.
Additional Notes
Applicants must be eligible to work in the US.
This role requires working from our Cambridge, MA or New York, NY office five days per week.
