Defense / Edge Tech Lead

Requisitos e responsabilidades

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What You'll Do

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.

You'll Love This Role If You

You find deep satisfaction in making a 300M-parameter model run on hardware with 4GB of RAM — and still hit accuracy targets.
You want to work at the intersection of AI and hardware, where optimization is not optional but existential.
You are energized by partnerships with hardware companies and enjoy the back-and-forth of getting a model to sing on a new chipset.
You understand the unique dynamics of defense and government customers and can navigate their requirements without losing engineering velocity.
You believe that edge AI is the next major deployment frontier, and you want to define how speech AI gets there.
You prefer working on hard, constrained problems over open-ended research — you want to ship, not just publish.

It's Important To Us That You Have

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.

It Would Be Great if You Had

Prior experience working on or alongside classified defense programs — you understand SCIFs, accreditation processes, and the operational constraints of secure environments, even if you do not currently hold an active clearance.
Experience with ML model optimization techniques at depth — custom quantization schemes, mixed-precision inference, neural architecture search for edge targets.
Familiarity with ONNX, TensorRT, or similar model compilation and optimization toolchains and their tradeoffs across hardware targets.
Defense or govtech industry experience, including familiarity with procurement processes, ITAR, FedRAMP, or DoD software development standards.
Experience with real-time audio processing on embedded platforms — DSP pipelines, audio codec optimization, or streaming inference on microcontrollers or edge SoCs.
Background in hardware evaluation and benchmarking — systematically comparing accelerators, SoCs, or GPUs for specific workload profiles.