Founding AI Engineer

Our Client - A US-based startup who is the neutral routing layer for voice AI. One API, every provider (STT/TTS/V2V), routed by language, latency, cost, and quality. Built for multilingual voice agents.

Responsibilities

• Own and ship Company's Voice Reliability Index (VRI), our public weekly-updated benchmark across voice providers (OpenAI, Google, ElevenLabs...) measuring p50/p95 latency, successful-turn rate, language-specific WER/MOS, cost per minute.
• Build the recommendation engine that powers Company's core value prop: when a customer onboards (a YC voice-agent dev, a contact-center vendor, a multilingual app), We answers "for your specific use case, the optimal stack is X with system prompt Y and tool-call config Z" - with a benchmark-backed guarantee.
• Lead TTS voice-identity preservation across providers. Decide build-vs-partner, staff and ship.
• Define the canonical metric system (routed turns, success-rate, latency budget) used across product, deck, dashboard, investor updates.
• Expand benchmarks to multilingual coverage (Indonesian, Vietnamese, Cantonese, Thai, Uzbek)
• Work directly with the CEO and the 4-person founding team. Daily async with engineering in Central Asia
• Ship public artifacts weekly: leaderboard updates, methodology posts, regression cards, incident postmortems. The benchmark is also the marketing engine.
• Own the public face of Company's eval credibility on Twitter, GitHub, Hugging Face. Be quotable.

Qualifications

• 5-8+ years building production AI/ML systems, ideally as a founding/early engineer at an AI-native or voice/speech startup.
• Direct experience with real-time voice agent pipelines (STT -> LLM -> TTS over WebRTC / WebSocket).
• Has built or shipped voice agents in production, not just experimented. Pipecat / LiveKit familiarity is a strong plus.
• Strong evaluation / benchmarking instincts: has shipped public benchmarks, contributed to leaderboards (Hugging Face Open ASR, TTS Arena, LMSYS, SEA-HELM), or built internal eval pipelines that drove product decisions.
• Familiar with RAGAS / DeepEval / Promptfoo / TruLens.
• Multi-LLM workflow experience: has built pipelines that route subtasks to different models (e.g. GPT-4o for vision, Gemini for spatial, Claude for reasoning). Understands the "right model per use case" pattern.
• Statistical and methodological rigor: percentiles not averages, reproducibility, version control for datasets, environmental robustness.
• Open-source / public-shipping track record: GitHub repos with stars, technical blog posts, public benchmarks, conference talks, or active Twitter/X presence in the AI/voice community.
• AI-native operator: daily user of Claude Code, Cursor, GPT, agent workflows. Treats benchmarking + research as engineering problems automatable with agents.

What you bring

Required skills

• Tech stacks: Python, TypeScript, ML evaluation, statistical methodology, voice AI pipelines (STT/TTS/LLM), WebRTC/WebSocket, LiveKit/Pipecat, OpenAI/Anthropic/Google APIs, Hugging Face, GitHub Actions, Docker.
• Industry: voice AI infrastructure, AI-native B2B, dev tools, speech tech, conversational AI.
• Language: fluent English (written + spoken). Bahasa Indonesia native a plus for benchmark dataset curation in regional languages.
• Comfortable being a public technical voice for the company on Twitter, GitHub, Hugging Face, Discord.

Preferred skills

• Pipecat, LiveKit, Daily.co, RAGAS, DeepEval, Promptfoo, TruLens, Hugging Face

Work arrangement

• Indonesia-based, with ~3 hours of daily overlap with US Pacific (early morning local time)
• Fully remote