Staff Product Engineer

About the role

This role leads the Product Engineering team to build audio data products that help customers prepare and understand speech data for model training. The position drives full-stack feature delivery, scalable data pipelines, and deployment of DSP and LLM-based solutions. It interfaces closely with researchers and operations to iterate on data collection while mentoring engineers as the team grows.

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

• Rapid full-stack feature delivery fuels product teams by shipping new capabilities to users daily.
• Scalable systems transform terabytes of audio, producing actionable insights for high-volume data pipelines.
• Solutions that blend large language models with digital signal processing refine customer data understanding and are evaluated for effectiveness.
• Technical standards guide engineering teams, ensuring consistent practices and quality as the team scales.
• Fluency in modern frameworks integrates current advances across software engineering, data engineering, machine learning, and signal processing.

Requirements

• Product-focused full-stack engineering experience of 6+ years is required for similar roles.
• Strong web development fundamentals enable rapid prototyping and scalable user solutions.
• A proven track record shows delivery of engineering solutions that provide clear customer value.
• Detail-oriented execution defines work in fast-paced environments.
• Intuitive, highly polished production-grade user experiences are a consistent focus.
• Success in technical leadership or technical leadership roles is demonstrated through track records.
• Willingness to learn AI/ML or audio concepts is essential even when direct experience is limited.
• US citizenship or permanent residency is a mandatory requirement for this position.

Practical notes

• The role is fully onsite in San Francisco with unlimited PTO.
• Health, dental, and vision coverage is provided with high plan coverage.
• Team tools include Next.js, TypeScript, TailwindCSS, Node.js, tRPC, PostgreSQL, AWS, Trigger.dev, WebRTC, and FFmpeg.
• 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

Speech data is handled at scale, with terabytes processed by teams. Production-grade user experiences are shipped daily using modern web and ML stacks. Technical leadership operates in fast-paced, ambitious environments.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

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.