SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Protege is building a platform to solve AI's data problem by facilitating secure, efficient, and privacy-centric exchange of AI training data. As a Forward Deployed Engineer (FDE) for the audio vertical, you will own the technical success for all audio customers while building reusable infrastructure for future scale.
You will partner closely with the audio vertical's GM, product and platform engineering teams, Data Lab, and commercial stakeholders to understand customer requirements and develop solutions. This is a high-ownership role where you'll manage customer requests, newly ingested datasets, and architecture decisions across multiple concurrent deals.
Key responsibilities include: developing solutions to transform raw, unstructured audio into organized, ready-to-sell datasets; rapidly characterizing large audio datasets to determine fit for customer use cases; owning customer engagements end-to-end from feasibility through post-delivery support; translating customer model-development goals into executable technical plans; and identifying patterns that should become shared platform capabilities.
You'll establish the audio vertical's technical playbooks and quality standards, working to scale the function with each new request. The role requires comfort with ambiguity, fast pace, multiple concurrent priorities, and availability outside standard hours when deals are live.
Success milestones: by 30 days, learn the platform and map tooling gaps; by 60 days, operate an active deal and build tools from customer requests; by 90 days, serve as default technical owner across the audio portfolio and establish the FDE playbook.
Required: 3+ years as an engineer with customer/stakeholder exposure; experience with media or audio data; experience building and operating data processing systems at scale; strong customer-facing ability; demonstrated end-to-end ownership; high ambiguity tolerance and bias to action.
Nice to have: early-stage startup experience; Python proficiency; product engineering mindset; experience with search, vector embeddings, semantic retrieval, or ML-assisted curation; AWS, Dagster, Databricks, or Vercel experience; audio processing, speech/music ML, transcription, diarization, or privacy workflow experience.
