Defense – Edge Tech Lead

Deepgram

. 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.

Posted 4/21/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $195,000 - $260,000 per yearWebsite

Tech Stack

Tools & technologies

AWSRust

About the role

Key responsibilities & impact

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.
Harden deployments for security-sensitive environments, implementing secure boot chains, encrypted model storage, tamper detection, and audit logging appropriate for defense and government use cases.
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

What you’ll need

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.
Familiarity with edge inference runtimes such as ONNX Runtime, TensorRT, TFLite, or vendor-specific SDKs (Qualcomm SNPE/QNN, MediaTek NeuroPilot, etc.).
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.
Excellent communication skills — you will be the technical face of Deepgram to hardware partners and defense customers, and you need to be credible and clear in both contexts.

Benefits

Comp & perks

Medical, dental, vision benefits
Annual wellness stipend
Mental health support
Life, STD, LTD Income Insurance Plans
Unlimited PTO
Generous paid parental leave
Flexible schedule
12 Paid US company holidays
Quarterly personal productivity stipend
One-time stipend for home office upgrades
401(k) plan with company match
Tax Savings Programs

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

CC++Rustmodel optimizationquantizationpruningknowledge distillationedge inference runtimessecure bootencrypted storage

Soft Skills

communicationtechnical leadershipmentoringcollaboration