Voice AI Platform · San Francisco
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. 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.
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. 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.
Deepgram is seeking a Senior Technical Program Manager (AI Tooling & Systems) to drive execution of large-scale ML infrastructure and AI tooling initiatives. In this role, you'll own the end-to-end delivery of programs that span model serving infrastructure, ML pipelines, internal AI tooling, and real-time inference systems—working closely with our ML engineers, research teams, and product to unlock capability at scale.
You'll thrive here if you enjoy creating clarity around complex ML system tradeoffs, building tools and processes that accelerate model development and deployment, and partnering across research, engineering, and product to align on technical strategy and execution.
Own end-to-end delivery of AI infrastructure programs—from model training pipelines and experiment tracking to inference serving and production monitoring
Define technical architecture, integration patterns, and rollout strategies for new ML systems and tooling (e.g., vector databases, model servers, evaluation frameworks, prompt engineering platforms)
Serve as connective tissue between ML research, ML engineering, product, and data teams to align on ML system requirements, capability roadmaps, and deployment timelines
Drive cost and latency optimization for real-time inference workloads at scale
Build lightweight internal tools and processes to accelerate ML iteration cycles (experiment tracking, model versioning, A/B testing infrastructure)
Identify and resolve technical bottlenecks in training pipelines, serving infrastructure, and model evaluation workflows
Work closely with ML practitioners to translate research breakthroughs into scalable, observable systems
Are passionate about building ML systems and infrastructure that powers frontier AI applications
Enjoy optimizing inference cost, latency, and throughput for LLM and multimodal workloads at scale
Love solving hard problems at the intersection of ML research and production systems (e.g., distillation, quantization, batching strategies)
Are excited about frontier model serving technologies, vector search, and real-time ML inference
Want to directly enable ML researchers and engineers to iterate faster and ship better models
5+ years of program management or technical leadership in ML infrastructure, ML platforms, or AI tooling (or equivalent)
Strong technical acumen in ML systems—ideally hands-on experience as an ML engineer, systems engineer, or ML infrastructure engineer
Experience coordinating cross-functional ML programs (e.g., model training → evaluation → serving → monitoring)
Proven ability to translate ML/research requirements into robust, scalable infrastructure
Comfortable working in ambiguity and helping teams navigate complex technical tradeoffs (e.g., accuracy vs. latency vs. cost)
Excellent communication with both technical and non-technical stakeholders
Familiarity with high-growth or startup environments
Hands-on experience with model serving frameworks (vLLM, TensorRT, TorchServe, or similar)
Experience optimizing LLM or speech/audio model inference (quantization, distillation, KV-cache optimization, batching strategies)
Familiarity with ML experiment tracking and versioning tools (MLflow, Weights & Biases, DVC, or similar)
Background in feature stores, vector databases, or real-time ML systems
Knowledge of cost optimization for GPU/ML workloads on cloud and on-premise infrastructure
Experience with multi-region model serving or edge deployment
Hands-on with relevant frameworks (PyTorch, CUDA, Hugging Face, etc.) or cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)