Deepgram
Defense / Edge Tech Lead
USA | Remote
No sponsorshipDetected 92 days ago
C++AWSMachine LearningCybersecurityProcurementSystems EngineeringSignal ProcessingResearchLeadershipCommunicationCollaborationMentoring
About the role
- Deepgram's voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency.
- There is no organization in the world that understands voice better than Deepgram.
- At Deepgram, we expect an AI-first mindset-AI use and comfort aren't optional, they're core to how we operate, innovate, and measure performance.
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.
- Benchmark and validate performance across target hardware platforms, establishing repeatable test suites for latency, accuracy, power consumption, and resource utilization.
- Collaborate with Research and Engine teams to influence model architectures toward edge-friendly designs from the start, reducing the optimization burden at deployment time.
- Provide technical leadership to cross-functional teams working on defense and edge projects, setting engineering standards, reviewing designs, and mentoring engineers on systems and optimization practices.
Requirements
- 5+ years of experience in systems engineering, embedded computing, or edge AI deployment, with a track record of delivering production systems on constrained hardware.
- Strong proficiency in C, C++, and/or Rust, with experience writing performance-critical code for resource-constrained environments.
- Hands-on experience with model optimization for edge deployment, including quantization, pruning, knowledge distillation, or architecture-specific compilation.
- Experience with security-conscious development practices, including secure boot, encrypted storage, code signing, and secure deployment pipelines.
- Strong understanding of hardware-software interaction - CPU/GPU/NPU architectures, memory hierarchies, power management, and how they affect model inference performance.
- Experience with ML model optimization techniques at depth - custom quantization schemes, mixed-precision inference, neural architecture search for edge targets.
- Experience with real-time audio processing on embedded platforms - DSP pipelines, audio codec optimization, or streaming inference on microcontrollers or edge SoCs.
- You are energized by partnerships with hardware companies and enjoy the back-and-forth of getting a model to sing on a new chipset.
Compensation
- Annual wellness stipend
Company info
- You will be the technical point of contact for some of Deepgram's most strategically important partnerships and customers.
- You understand the unique dynamics of defense and government customers and can navigate their requirements without losing engineering velocity.
Visa & Work Authorization
- Note that Deepgram does not currently hold facility clearance — this role does not require an active security clearance, though experience working in or alongside classified programs is highly valued
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This listing is sourced directly from Deepgram's careers page and normalized into a canonical job model.