Tech Stack
Responsibilities
- Lead the technical strategy for edge deployment of Deepgram's STT and TTS models, defining the architecture for on-device, on-premises, and air-gapped inference across diverse hardware targets.
- Optimize models for edge and embedded platforms, driving quantization, pruning, distillation, and runtime optimization to meet strict latency, memory, and power constraints.
- Partner with Qualcomm, Motorola, and other hardware vendors to ensure Deepgram models run efficiently on their chipsets, collaborating on SDK integration, performance benchmarking, and joint go-to-market.
- Support defense customer requirements through AWS NatSec partnerships, translating mission requirements into engineering deliverables and ensuring Deepgram's solutions meet the unique demands of government environments.
- Design and build edge runtime infrastructure, including model packaging, deployment pipelines, OTA update mechanisms, and telemetry for devices operating in low-connectivity or disconnected environments.
Benefits
- 401k
- Flexible Hours
- Gym Membership
- Health Insurance
- Learning Budget
- Parental Leave
- Remote Stipend
- Unlimited PTO
Culture
AI-FirstFast-PacedExperimentationAdaptabilityContinuous Learning
Requirements
Regions: Us, Worldwide
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About Deepgram
Industry: ai
Size: small
Deepgram is the leading Voice AI platform providing real-time APIs for speech-to-text, text-to-speech, and building production-grade voice agents, trusted by over 200,000 developers and 1,300+ organizations. The company's voice-native foundation models offer unmatched accuracy, low latency, and cost efficiency, having processed over 50,000 years of audio.
View company profile →Compensation
Equity: Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $215M in total funding.