Product Engineer
Job description
About the role
This role centers on constructing data infrastructure and tools that convert raw audio into high-signal datasets for AI labs and enterprises. You will collaborate with researchers and operations to design, iterate, and refine features that serve thousands of users daily. The work drives scalable data pipelines and rapid experimentation that advance audio AI research.
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
Production teams release full-stack features that reach thousands of users daily, enabling data-driven product improvements.
Systems process terabytes of raw audio each day to extract actionable insights, powering scalable data pipelines for research.
Solutions built with LLM and DSP methods deepen customer understanding of nuanced dataset characteristics and quality.
Operations and research teams deploy data collection interfaces to accelerate hypothesis testing and validate new audio use cases.
General audio data research converts raw sound into structured training datasets for AI models using modern software stacks.
Requirements
Candidates bring 2+ years of product-focused full-stack engineering experience to contribute across the stack.
Strong full-stack web fundamentals enable rapid prototyping, scaling to many users, and maintaining reliable production systems.
A history of delivering engineering solutions that create customer value is essential for success in this role.
Detail oriented execution sustains constant progress in a high-pace environment while maintaining quality and reliability.
Intuitive products and polished production-grade experiences resonate with users and meet demanding standards.
Background in digital signal processing and deep speech knowledge is preferred for consideration to address audio-specific challenges.
Experience building and deploying ML models in production is preferred to support data pipeline and model workflows.
Leadership or tech-led experience managing engineering teams in high-growth settings is a bonus for cross-functional coordination.
Practical notes
The role is based in San Francisco and requires a degree. Candidates must be eligible to work in the United States without sponsorship. Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Audio data research converts raw sound into structured training datasets for AI models. Teams typically use tools like Next.js, TypeScript, and PostgreSQL to deliver data products. Signal processing and production-grade web development define the day-to-day work in this field.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.