Forward Deployed Engineer, Audio

Stack

Responsibilities

• ▹Develop and own solutions that turn raw audio, often with unstructured or missing metadata, into organized, ready-to-sell datasets
• ▹Build prototypes and phase them into reliable production systems that others can understand and operate
• ▹Rapidly characterize large, unfamiliar audio datasets and determine whether they can support a customer's use case
• ▹Work with the audio vertical GM, core technical teams and commercial stakeholders from feasibility through post-delivery support
• ▹Translate a customer's model-development goals into an executable technical plan with clear acceptance criteria
• ▹Own the implementation and operation of the engagements you lead
• ▹Work with the product and core engineering teams to identify areas of platform growth based on customer requests and learnings from the front lines
• ▹Identify recurring patterns that should become shared cross-vertical platform capabilities and help design and build them
• ▹Build the audio vertical's technical playbooks and quality standards so the function scales with every new request
• ▹First 30 days: learn the platform, the audio data landscape, the customer and partner portfolio and the processing and delivery systems; pair with an engineer on an existing audio deal; map the largest gaps in tooling and the operating model and propose a prioritized plan
• ▹By 60 days: run an active audio deal as the primary FDE; build or meaningfully extend a tool or workflow that came out of an active customer request; establish a communication cycle with other FDEs and product to surface learnings worth generalizing
• ▹By 90 days: serve as the default technical owner across the audio vertical's active portfolio, including multiple active deals; own end-to-end architecture and delivery including post-delivery support and iteration; establish the first version of the audio FDE playbook and reusable toolkit; maintain a concrete roadmap of platform investments to increase delivery capacity

Requirements

• ▹3+ years of experience as an engineer, including meaningful exposure to customers or external technical stakeholders
• ▹Experience working directly with media data (direct work with audio data is a bonus)
• ▹Experience building and operating systems that process, analyze or deliver data at scale
• ▹Customer-facing ability, including translating ambiguous requirements, communicating trade-offs and building trust with technical stakeholders
• ▹Demonstrated end-to-end ownership, from initial problem definition through implementation, validation and support
• ▹High ambiguity tolerance and a bias to action, with the judgment to know when to investigate further or push back
• ▹Comfort with the intensity and pace of a fast-moving environment, including multiple concurrent priorities and time-sensitive customer work

Nice to have

• ▹Experience at an early-stage company, as a founding or early engineer, or in another startup-like environment with broad ownership
• ▹Hands-on experience with Python
• ▹Product engineering experience or a strong product mindset developed in close partnership with users
• ▹Experience with search, vector embeddings, semantic retrieval or ML-assisted data curation
• ▹Experience with the company's cloud and data infrastructure (AWS, Dagster, Databricks, Vercel)
• ▹Experience with audio processing, speech or music ML, transcription, diarization, annotation, quality measurement, or privacy and redaction workflows

Soft skills

About the company

Protege builds a platform for the secure, efficient and privacy-centric exchange of AI training data. It is a lean, fast-moving team backed by leading investors and already partnering with ambitious AI teams.