Staff Full Stack Engineer

About the role

This role leads full-stack engineering for audio data products that help customers prepare and understand audio for model training. You will own feature delivery, scalable data pipelines, and deployment of DSP and LLM solutions. Collaboration with researchers and Operations drives rapid iteration in data collection and interface design.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Architect and iterate full-stack features that deliver daily innovations to users in audio data research. Production systems translate raw audio into high-signal datasets that power model training for customers.

Build scalable systems that process terabytes of audio data and derive actionable insights for training and operations. Data pipelines ingest and transform massive audio streams to support research and product workflows.

Deploy and evaluate language model and signal processing solutions that enhance customer understanding of audio data. Evaluations refine how customers interact with and interpret audio content within their workflows.

Research and operations teams align collection strategies through shared interfaces and rapid experimentation.

Engineering standards emerge through collaboration as the platform and product teams grow.

Fluency ensures integration of modern tools into production-grade data platforms.

Requirements

Bring 6+ years of product-focused full-stack engineering experience in audio data or related domains.

Show strong web development fundamentals for building rapid prototypes and scalable user solutions.

Prove a track record of delivering engineering solutions that provide customer value in production environments. Production impact reflects consistent execution on audio data workflows and user needs.

Thrive in fast-paced settings with detail-oriented execution on complex audio data workflows. Fast-paced contexts demand precision in handling evolving requirements and data complexity.

Focus on building intuitive, highly polished production-grade user experiences for data products. Experiences reduce friction for researchers and operations teams during data collection.

Document a track record of success in technical leadership or engineering management. Leadership shapes how teams coordinate, prioritize, and execute demanding audio data initiatives.

Willingness to learn audio, AI/ML, and DSP concepts is essential even without prior domain experience. Eagerness to learn supports rapid adaptation to audio-specific challenges and tooling.

Nice to have

Educational or professional exposure to digital signal processing and deep understanding of speech. Such exposure deepens insight into audio characteristics and processing techniques.

Experience building and deploying ML models in production at scale. Production ML experience informs how models integrate with data collection and processing pipelines.

Led managerial or technical leadership of engineering teams in high-growth environments. High-growth settings require adaptability in leadership style and technical decision-making.

Skills & tools

Work with Next.js, TypeScript, TailwindCSS, Node.js, tRPC, PostgreSQL, AWS, Trigger.dev, WebRTC, and FFmpeg to ship audio data platforms. These tools support reliable data ingestion, processing, and delivery at scale.

Practical notes

Role is based in San Francisco with unlimited PTO and comprehensive health coverage. Team operates in a fast-moving, research-driven environment focused on audio data infrastructure. Good to know

Work in this role involves intensive audio data research and full-stack engineering responsibilities. Success requires fluency in modern web tools and audio processing concepts. The environment emphasizes fast iteration and close collaboration across research and operations teams. Expect exposure to production-scale data pipelines and generative audio technologies. Confirm expectations through official company resources before committing.