other
Posted Apr 21Defense / Edge Tech Lead
at Deepgram
United StatesRemote
Responsibilities
- - Optimize models for edge and embedded platforms, driving quantization, pruning, distillation, and runtime optimization to meet strict latency, memory, and power constraints.
- - 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.
- - Collaborate with Research and Engine teams to influence model architectures toward edge-friendly designs from the start, reducing the optimization burden at deployment time.
Requirements
- COMPANY OVERVIEW Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale.
- COMPANY OPERATING RHYTHM 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.
- Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work.
- We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here.
- Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.
- Additionally, we move at the pace of AI.
- THE OPPORTUNITY Deepgram's speech AI models are among the fastest and most accurate in the world — and an increasing number of defense and edge computing customers need those models to run outside of the cloud.
- This is the frontier where AI meets the physical world, and it requires a fundamentally different engineering approach.
- requirements through AWS NatSec partnerships, and drive the model optimization and runtime engineering needed to deliver production-quality speech AI on constrained platforms.
- experience working in or alongside classified programs is highly valued.
- 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.
- - 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.
- requirements through AWS NatSec partnerships, translating mission
- 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.
- 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.
- 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. BENEFITS & PERKS
- If you're looking to work on cutting-edge technology and make a significant impact in the AI industry, we'd love to hear from you! Deepgram is an equal opportunity employer.
Experience
- IT'S IMPORTANT TO US THAT YOU HAVE - 5+ years of
Benefits
- HOLISTIC HEALTH - Medical, dental, vision
- benefits - Annual wellness stipend - Mental health support - Life, STD, LTD Income Insurance Plans WORK/LIFE BLEND - Unlimited PTO - 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 CONTINUOUS LEARNING - Learning / Education stipend - Participation in talks and conferences - Employee Resource Groups - AI enablement workshops / sessions *For candidates
- Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $215M in total funding.
Additional details
- More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box.
- 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.
- Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words.
- There is no organization in the world that understands voice better than Deepgram.
- Change is rapid, and you can expect your day-to-day work to evolve just as quickly.
- This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.
- On devices, on-premises, in disconnected environments, and on hardware with strict power and compute constraints.
- As the Defense / Edge Tech Lead, you will own the technical direction for deploying Deepgram's models to edge and embedded environments.
- You will work closely with hardware partners like Qualcomm and Motorola, support defense customer
- You will be the technical point of contact for some of Deepgram's most strategically important partnerships and customers.