ML Ops Infrastructure Engineer

Deepgram·USA | Remote·6 апр.

ML Ops Infrastructure Engineer

≈ от 180 000 ₽наша оценка по вакансиям этой роли и грейда, у работодателя вилка не указана

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Наша компания

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.

О роли

Getting a model from a research notebook to a production API serving millions of requests is one of the hardest problems in AI. As an ML Ops Infrastructure Engineer at Deepgram, you will own the critical bridge between research and production -- building the pipelines, deployment systems, and testing infrastructure that take models from experimental to battle-tested at scale. Your work ensures that every model improvement our research team makes can be safely, quickly, and reliably delivered to the customers who depend on Deepgram's APIs for real-time voice AI

Чем предстоит заниматься

Design and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment

Architect and maintain model deployment pipelines that move models from research environments through staging to production with confidence

Build A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact

Implement comprehensive monitoring for model performance in production -- accuracy metrics, latency, drift detection, and regression alerts

Develop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences

Create and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment

Establish model versioning, artifact management, and rollback capabilities to ensure safe and reproducible deployments

Collaborate with research engineers to define and enforce model quality gates before production promotion

Build observability dashboards that give the team real-time insight into model health across all environments

Optimize model serving infrastructure for latency, throughput, and cost efficiency

Наши требования

4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems

Strong proficiency in Python and experience building automation and tooling for ML workflows

Deep experience with CI/CD systems and building pipelines for software and model delivery

Hands-on experience with Docker and Kubernetes for containerized workload management

Practical experience deploying and serving ML models in production environments

Familiarity with model evaluation, validation, and quality assurance processes

Understanding of monitoring and observability principles as applied to ML systems

Strong problem-solving skills and a bias toward automation over manual processes

Experience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime

Background in speech, audio, or real-time media ML systems

Experience with Infrastructure as Code tools such as Terraform or Pulumi

Hands-on experience with monitoring and observability stacks (Prometheus, Grafana, Datadog, or similar)

Familiarity with GPU-accelerated inference optimization and profiling

Experience with feature stores, data versioning, or ML metadata management

Knowledge of canary deployment strategies and progressive delivery for ML models

Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an .com email address. If you've received a message claiming to be Deepgram, please forward it to careers .com

Дополнительно

Are excited by the challenge of operationalizing cutting-edge AI models at production scale

Believe that great infrastructure is what turns research breakthroughs into customer value

Enjoy designing systems that are automated, reliable, and self-healing

Want to work on problems where minutes of latency reduction or percentage points of accuracy matter enormously

Like collaborating across research and engineering teams to make the whole organization faster

Are motivated by building the deployment and testing systems that back a platform serving over 200,000 developers

Deepgram

USA | Remote

ГрейдMiddle

ЗанятостьПолная занятость

РегионСША

ФорматУдалённо

ИсточникСкрыто

Опубликовано6 апр.

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