技术项目经理 (工程) - AI工具与系统
Technical Program Manager (Engineering) - AI Tooling & Systems
## **公司简介**
Deepgram 是支撑新兴数千亿美元语音 AI 经济的领先平台，提供实时 API 服务，包括语音转文本（STT）、文本转语音（TTS），以及大规模构建生产级语音代理。超过 20 万名开发者和 1,300 多家组织使用“由 Deepgram 提供支持”的语音产品，包括 Twilio、Cloudflare、Sierra、Decagon、Vapi、Daily、Cresta、Granola 和 Jack in the Box。Deepgram 的语音原生基础模型通过云 API 或自托管及本地软件访问，具有无与伦比的准确性、低延迟和成本效率。在由领先的全球投资者和战略合作伙伴主导的最新 C 轮融资支持下，Deepgram 已处理超过 5 万年的音频，并转录了超过 1 万亿个单词。世界上没有哪家公司比 Deepgram 更了解语音。
## **公司运营节奏**
在 Deepgram，我们期望具备 AI 优先的思维模式——AI 的使用和熟悉度不是可选的，而是我们运作、创新和衡量绩效的核心。
每位在 Deepgram 工作的团队成员都应积极使用并试验先进的 AI 工具，甚至将它们融入日常工作中。我们衡量 AI 应用于实现结果的效果，持续且富有创意地利用最新 AI 能力是这里成功的关键。候选人应能够快速采用新模型和新模式，将 AI 整合到自己的工作流程中，并不断推动这些技术的边界。
此外，我们以 AI 的速度前进。变化迅速，你每天的工作内容也会随之快速变化。如果你对实验、适应、临场思考和不断学习不感兴趣，或者你寻求的是高度规范化的传统朝九晚五工作，那么这可能不是适合你的职位。
Deepgram 正在寻找一名 **高级技术项目经理（AI 工具与系统）** 来推动大规模 ML 基础设施和 AI 工具项目的执行。在这个职位上，你将负责从头到尾交付涵盖模型服务基础设施、ML 管道、内部 AI 工具和实时推理系统的项目——与我们的 ML 工程师、研究团队和产品团队紧密合作，实现能力的规模化。
如果你喜欢在复杂的 ML 系统权衡中创造清晰的思路，构建加速模型开发和部署的工具和流程，并与团队合作，那么你在这里会很出色。
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## **Company Overview**
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.
## **Company Operating Rhythm**
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) for Engineering** 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.
## What You'll Do
- 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
## You'll Love This Role If You
- 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
## It's Important That You Have
- 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
## It Would Be Great If You Had
- 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)
_**Notice**: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @_ [_deepgram.com_](http://deepgram.com) _email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com._