Audio Data Infrastructure Engineer (AI)

TL;DR

Audio Data Infrastructure Engineer (AI): Design and maintain a robust, scalable data pipeline that transforms raw audio from diverse sources into structured analytics at scale with an accent on database architecture, high-volume ingestion pipelines, and analysis and labeling workflows. Focus on performance, correctness, and fault tolerance across ingestion, processing, and storage layers.

Location: On-site in Berlin

Company

hirify.global is building the reliability layer for Voice AI, the system that closes the gap between raw audio input and reliable machine understanding in production.

What you will do

• 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.
• 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.
• 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.
• A startup mindset: You’re comfortable with ambiguity, take ownership of complex systems, and make pragmatic decisions in a fast-moving, product-driven environment.

Nice to have

• 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.
• Prior startup or similarly dynamic experience is a strong plus.

Culture & Benefits

• Opportunity to work at a rapidly growing Voice AI startup, backed by top investors.
• Competitive salary package, additional benefits and stock options, enabling you to take part in the company’s success.
• Dynamic, fast-paced environment with passionate and collaborative colleagues.
• Groundbreaking startup at a pivotal growth stage, making a real difference in how people experience audio.
• Take full ownership of projects and ship fast.
• World-class team of engineers and builders with ample room for professional growth.