Applied ML Engineer - Edge Devices

• 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.
• 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.
• 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.
• Have already fought to get a model running on hardware that wasn't built for it, and want to do that across many platforms.
• Prefer changing the model to fit the hardware over changing the hardware to fit the model, and know when each is the right call.
• Care about the numbers on the device, not the numbers in the notebook.
• Like being the bridge between the team writing kernels and the team training models.
• Want to ship to customers, not publish.
• Hands-on experience deploying ML models to edge or non-NVIDIA hardware in production. This is required. Cloud-only or GPU-only serving experience does not qualify on its own.
• 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 (for example ONNX Runtime, TFLite, ExecuTorch, OpenVINO, Qualcomm AI Engine, or a 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 that others can rerun.
• 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.
• Experience with speech, audio, or streaming/real-time models specifically.
• Exposure to writing or reading low-level kernels (CUDA, Metal, NEON, or vendor DSP code), enough to collaborate closely with embedded engineers.
• Experience with model security or integrity on deployed devices: signing, encrypted model storage, safe updates.
• Familiarity with several accelerator families (Qualcomm, Apple, ARM, Intel, AMD, or custom NPUs) and their quirks.

None flagged.