Artificial Analysis, Inc.
Member of Technical Staff (Speech)
San Francisco (On-site) · Staff+
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About the role
- Speech is becoming AI's next interface: text to speech, speech to text, voice cloning and real-time voice agents are moving from demos into infrastructure, and our speech benchmarks are how the industry tracks who is winning.
Responsibilities
- Design Speech Evaluations: Build and extend the evaluation frameworks, prompt libraries and arenas that reflect how developers and creators actually use speech models, including agentic voice through AgentTalk
- Partner with Speech Leaders: Work with the top speech AI companies in the world at a deep technical level to benchmark their models and shape how the industry measures voice
- Drive Product Direction: Shape the roadmap of our speech benchmarking platform together with our engineers and pillar lead
- Become AI-Native: Embrace an AI-native workflow, using cutting-edge AI tools to generate leverage in a fast-changing industry and maintain our competitive edge in AI benchmarking
- Backgrounds include: product, research or engineering roles at speech AI companies (e.g. ElevenLabs, Cartesia, Inworld, Sesame, Deepgram, AssemblyAI, Rime or similar), voice teams at larger platforms (e.g. OpenAI, Google, Microsoft), or teams building products on text to speech, speech to text or real-time voice.
Requirements
- 3+ years of professional experience, including at least 1 year working hands-on with speech AI
- Proficiency in Python and data analysis
- Genuine, demonstrable interest and knowledge of Frontier AI.
Compensation
- Competitive compensation including equity
Benefits
- Benchmark the Speech Frontier: Own coverage across text to speech, speech to text and speech to speech, benchmarking new models and providers as they launch
Company info
- We are the go-to authority for understanding AI, from AI labs and enterprises to media, investors, and policymakers.
- What We're Looking For
This listing is sourced directly from Artificial Analysis, Inc.'s careers page and normalized into a canonical job model.