Staff Full-Stack Engineer: AI, Audio & Scalable Systems

Aurrum Services; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Aurrum Services is seeking a Staff Full Stack Engineer to lead end-to-end product development at the intersection of web technology, AI/ML, audio data, and research. You’ll own complex engineering initiatives from concept through production, collaborating with researchers, product teams, and Operations to rapidly build, test, and improve products used to understand large-scale audio datasets.

You will mentor engineers, drive architecture decisions, and help shape scalable, production-grade

Qualifications

• 6+ years of experience in product-focused full-stack software engineering.
• Experience taking products from prototype to production.
• Experience in fast-paced, high-growth environments with ownership.

Responsibilities

• Lead the development of full-stack product features from ideation through deployment.
• Design and build scalable systems capable of processing terabytes of audio data.
• Deploy and evaluate LLM- and DSP-based technologies to improve workflows and data understanding.
• Collaborate with researchers and Operations to iterate on data collection workflows and interfaces.
• Establish engineering standards, development practices, and processes as the team grows.
• Mentor engineers and provide hands-on technical leadership across projects.
• Continuously evaluate new technologies across software, data engineering, AI/ML, and signal processing.

Skills

Full-stack development

Technical leadership

Product mindset

Mentoring engineers

Cross-functional collaboration

Job description

Aurrum Services is seeking a Staff Full Stack Engineer to lead end-to-end product development at the intersection of web technology, AI/ML, audio data, and research. You’ll own complex engineering initiatives from concept through production, collaborating with researchers, product teams, and Operations to rapidly build, test, and improve products used to understand large-scale audio datasets.

You will mentor engineers, drive architecture decisions, and help shape scalable, production-grade