Full Stack Engineer

About the role

This role centers on building and maintaining the data infrastructure that converts raw audio into training-ready datasets. The work supports researchers and customers in analyzing audio datasets through evolving tools. The emphasis is on high-quality dataset creation and 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

Interfaces power user interactions with audio data tools, and these tools are built and iterated with researchers to support analysis of audio datasets. End-to-end ownership ships full-stack features that thousands of users interact with daily and drives product delivery for data platforms.

Requirements

The posting states a bachelor's degree requirement. 2+ years of full-stack engineering experience is mandatory for this role to ensure sufficient background for complex audio data challenges. Strong full-stack web development fundamentals are required to create rapid prototypes and scale solutions that serve research and enterprise users. A proven track record of delivering engineering solutions that create customer value is required to meet dataset and product expectations. Detail-oriented execution enables constant progress in a high-pace environment where precision affects data quality. Building intuitive products and experiences that resonate with users is a core expectation to support researcher and customer workflows. Educational or professional experience with digital signal processing and a deep understanding of speech is a bonus to handle audio-specific requirements. Experience building and deploying ML models in production is a bonus for maintaining reliable data pipelines. Led engineering teams in high-growth environments is a bonus to coordinate fast-moving delivery.

Practical notes

The role is based in San Francisco and requires a degree to ensure appropriate foundational knowledge. The position is full_time with unlimited PTO and comprehensive health coverage as described in benefits. to verify current requirements and opportunities.

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

The role works with audio data, speech, and signal processing using modern data and model pipelines. Common tools include Next.js, TypeScript, TailwindCSS, Node.js, tRPC, PostgreSQL, AWS, Trigger.dev, WebRTC, and FFmpeg. The environment emphasizes fast execution, ownership, and rapid iteration to support research and production goals.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

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.