USA | Remote
Remote
Senior
Full Time
6 days ago
💰$ 195,000 - $ 260,000
AIedge computingdefenseembedded systemsmodel optimizationC++Rustsystems engineering
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.
- •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.
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.
Nice to Have
- •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.
Benefits
- •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
- •Learning / Education stipend
- •Participation in talks and conferences
- •Employee Resource Groups
- •AI enablement workshops / sessions
About Deepgram
Deepgram specializes in providing AI-powered speech-to-text technology that offers audio intelligence, text-to-speech, and voice agent API.
San Francisco, CA
100 - 250
AI & Machine Learning