Role overview

We’re looking for an Audio Data Infrastructure Engineer to design and maintain a robust, scalable data pipeline that transforms raw audio from diverse sources into structured analytics at scale.

In this role, you’ll own the database architecture, high-volume ingestion pipelines, and analysis and labeling workflows that process many terabytes of audio. This includes ingesting raw audio, running large-scale ML- and DSP-based analysis, and storing the resulting metadata and analytics efficiently in a large PostgreSQL database.

Your work underpins our training, evaluation, and analysis workflows, and requires careful attention to performance, correctness, and fault tolerance across ingestion, processing, and storage layers. The role is on-site in Berlin.

Tasks

• Architect and maintain a large-scale PostgreSQL database optimized for analytical workloads.
• Design scalable ingestion pipelines for audio data from many sources.
• Build distributed compute pipelines for ML inference on audio frames.
• Design and maintain efficient metadata storage for audio, frames, statistics, and analysis results.
• Optimize ETL/ELT pipelines for performance, reliability, and scalability.
• Ensure idempotent, fault-tolerant workflows across ingestion and analysis.
• Work closely with ML and backend teams to integrate new models and analytics.

Requirements

• 3+ years of experience in Data Engineering, ML Infrastructure, or Distributed Systems, working on production systems at scale.
• Deep experience with PostgreSQL at scale, including schema design, partitioning, indexing, and high-throughput bulk loading.
• Experience building and operating reliable ETL pipelines, using tools such as Airflow, Prefect, Dagster, or custom frameworks.
• Strong Python engineering skills, including async processing, multiprocessing, and large-scale batch workflows.
• Experience processing very large datasets, on the order of hundreds of millions of rows or TB-scale files, with efficient storage and access patterns.
• Practical familiarity with audio data as a modality, including common processing tools (e.g. FFmpeg) and an understanding of how audio artifacts and preprocessing choices affect downstream analysis.
• Experience running ML inference pipelines at scale to label, classify, or structure large datasets, with a realistic understanding of what modern ML models can and cannot reliably infer.
• A startup mindset: You’re comfortable with ambiguity, take ownership of complex systems, and make pragmatic decisions in a fast-moving, product-driven environment. Prior startup or similarly dynamic experience is a strong plus.

Benefits

• Opportunity to work at a rapidly growing Voice AI startup, backed by top investors.
• Compensation and equity: Competitive salary package, additional benefits and stock options, enabling you to take part in the company’s success.
• Startup Culture: Dynamic, fast-paced environment with passionate and collaborative colleagues.
• High Impact: Groundbreaking startup at a pivotal growth stage, making a real difference in how people experience audio.
• Ownership & Autonomy: Take full ownership of projects and ship fast.
• Work With the Best: World-class team of engineers and builders with ample room for professional growth.
• Contribute to the Future: Define the landscape of Voice AI technology.

If you are ready to lead the charge in revolutionizing Voice AI and drive our startup to new heights, we would love to hear from you. Apply today to join the ai-coustics team!