Applied ML Engineer - Edge Devices
About the role
Applied ML Engineer focused on porting and adapting Deepgram speech models to non-NVIDIA and edge hardware, ensuring runtime compatibility, accuracy, and latency on target platforms.
Responsibilities
- Port Deepgram speech models to non-NVIDIA and edge platforms, adapting model structure and parameters so they run within the target's existing operator set, runtime, and kernels with minimal modification.
- Own serving-side model decisions for edge targets: quantization and precision choices, operator substitution, graph rewrites, and architecture tweaks that fit a model to a device's constraints while holding accuracy and latency.
- Validate every port on real hardware: build accuracy, latency, throughput, and memory benchmarks per platform, and catch regressions before a customer does.
- Build the deployment path for edge targets: model packaging, conversion pipelines, versioning, and automated delivery so shipping a model to a new device is repeatable rather than bespoke.
- Work with Embedded AI Engineers when a standard kernel isn't enough: specify what the model needs, then adapt the model to use the custom kernel they deliver.
- Partner with platform and silicon vendors on their runtimes and toolchains, and turn their expected model format and operator conventions into a working Deepgram deployment.
- Feed edge constraints back to Research and Impeller so future models are easier to port, without taking on research or core productionization work yourself.
- As the team grows, take on adjacent production concerns at the edge: automated deployment, model security and integrity on customer hardware, and fleet-level observability.
Qualifications
- Hands-on experience deploying ML models to edge or non-NVIDIA hardware in production.
- Working knowledge of quantization and precision tradeoffs (INT8, FP16, mixed precision, calibration) and how they affect accuracy and latency on real targets.
- Experience with at least one edge or vendor inference runtime and its conversion toolchain (e.g., ONNX Runtime, TFLite, ExecuTorch, OpenVINO, Qualcomm AI Engine, or vendor NPU SDK).
- Ability to modify a model to fit a platform: reading and rewriting model graphs, swapping unsupported operators, and adjusting architecture parameters without breaking accuracy.
- Strong Python and PyTorch, and production-quality engineering habits: tests, reproducibility, and benchmarks.
- Comfort building automation around model conversion and deployment.
- A builder mindset and clear communication: you can scope a port on an unfamiliar platform and drive it to a measured result.
Skills
Benefits
- Equity
- Bonus (Annual)
- 10% Annual Bonus
- Estimated Base Salary $155K – $245K
About Deepgram
Building foundational AI for speech transcription and understanding.
Deepgram is a foundational AI company on a mission to understand human language. We give any developer access to the most advanced speech AI transcription and understanding with just an API call. Our models deliver the fastest, most accurate transcription alongside contextual features like summarization, sentiment analysis, and topic detection. Beyond that, developers can: 🔊 Process live-streaming or pre-recorded audio 🌎 Transcribe in dozens of languages ⚙️ Train custom models for unique use cases 🔑 Access deep NLU with a unified API 💻 Build in any programming language with our SDKs ✅ Deploy on-prem or on DG’s managed cloud 📈 Get scalable GPU infra for training and inference Deepgram is a proud NVIDIA partner and Y Combinator company, and we recently completed a $72M Series B to define the future of AI Speech Understanding, making us the most-funded speech AI company at its stage.
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