Applied ML Engineer (Edge AI)

Location: USA — Remote

Base salary: $155,000–$245,000 per year, plus equity, bonus, and benefits.

Company

Deepgram provides real-time speech-to-text, text-to-speech, and voice-agent APIs and deployable voice AI models for developers and organizations.

What you will do

• Port speech models to non-NVIDIA accelerators, edge servers, and embedded platforms with minimal model changes.
• Make serving-side decisions involving quantization, precision, operator substitution, graph rewrites, and architecture adjustments.
• Validate ports on real hardware using accuracy, latency, throughput, and memory benchmarks.
• Build repeatable model packaging, conversion, versioning, and automated deployment pipelines for edge targets.
• Collaborate with embedded engineers, platform vendors, and silicon partners on kernels, runtimes, and toolchains.
• Help address edge deployment concerns such as model security, integrity, automated delivery, and observability.

Requirements

• Production experience deploying machine learning models to edge or non-NVIDIA hardware; cloud-only or GPU-only serving experience is insufficient.
• Working knowledge of quantization and precision trade-offs, including INT8, FP16, mixed precision, and calibration.
• Experience with an edge or vendor inference runtime and conversion toolchain such as ONNX Runtime, TFLite, ExecuTorch, OpenVINO, Qualcomm AI Engine, or a vendor NPU SDK.
• Ability to read and rewrite model graphs, replace unsupported operators, and adjust architecture parameters while preserving accuracy.
• Strong Python and PyTorch skills, with production-quality testing, reproducibility, benchmarking, and deployment automation.
• A builder mindset and ability to scope and deliver ports on unfamiliar platforms.

Nice to have

• Experience with speech, audio, or streaming and real-time models.
• Exposure to low-level kernels such as CUDA, Metal, NEON, or vendor DSP code.
• Experience with model signing, encrypted storage, safe updates, or other deployed-model security measures.
• Familiarity with Qualcomm, Apple, ARM, Intel, AMD, or custom NPU accelerator families.
• Experience building internal tooling that improves model porting or deployment speed.

Culture & Benefits

• AI-first working environment with active use and experimentation with advanced AI tools.
• Fast-changing work focused on experimentation, adaptation, and continuous learning.
• Opportunity to ship applied machine learning models to customers across multiple hardware platforms.
• Equity, annual bonus, and benefits are offered in addition to the base salary.