{"schemaVersion":"jobsearcher.job.v1","id":"6f76781f817076fa7bc45d99","url":"https://jobsearcher.com/jobs/6f76781f817076fa7bc45d99","canonicalUrl":"https://jobsearcher.com/jobs/6f76781f817076fa7bc45d99","title":"ML Ops Infrastructure Engineer","description":"Location\nUSA | Remote\nEmployment Type\nFull time\nLocation Type\nRemote\nDepartment\nEngineering\nCompensation\n$160K – $220K • Offers Equity • Offers Bonus\nThis range is determined by work location and additional factors, including job-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.\n\nPlease note that the compensation details listed on US role postings reflect the base salary only and does not include bonus, equity or benefits.\n\nCompany Overview\nDeepgram 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.\nCompany Operating Rhythm\nAt 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.\nEvery 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.\nAdditionally, 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.\nThe Opportunity\nGetting 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.\n\nWhat You'll Do\nDesign and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment\nArchitect and maintain model deployment pipelines that move models from research environments through staging to production with confidence\nBuild A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact\nImplement comprehensive monitoring for model performance in production - accuracy metrics, latency, drift detection, and regression alerts\nDevelop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences\nCreate and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment\nEstablish model versioning, artifact management, and rollback capabilities to ensure safe and reproducible deployments\nCollaborate with research engineers to define and enforce model quality gates before production promotion\nBuild observability dashboards that give the team real-time insight into model health across all environments\nOptimize model serving infrastructure for latency, throughput, and cost efficiency\n\nYou'll Love This Role If You\nAre excited by the challenge of operationalizing cutting-edge AI models at production scale\nBelieve that great infrastructure is what turns research breakthroughs into customer value\nEnjoy designing systems that are automated, reliable, and self-healing\nWant to work on problems where minutes of latency reduction or percentage points of accuracy matter enormously\nLike collaborating across research and engineering teams to make the whole organization faster\nAre motivated by building the deployment and testing systems that back a platform serving over 200,000 developers\n\nIt's Important To Us That You Have\n4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems\nStrong proficiency in Python and experience building automation and tooling for ML workflows\nDeep experience with CI/CD systems and building pipelines for software and model delivery\nHands-on experience with Docker and Kubernetes for containerized workload management\nPractical experience deploying and serving ML models in production environments\nFamiliarity with model evaluation, validation, and quality assurance processes\nUnderstanding of monitoring and observability principles as applied to ML systems\nStrong problem-solving skills and a bias toward automation over manual processes\n\nIt Would Be Great If You Had\nExperience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime\nBackground in speech, audio, or real-time media ML systems\nExperience with Infrastructure as Code tools such as Terraform or Pulumi\nHands-on experience with monitoring and observability stacks (Prometheus, Grafana, Datadog, or similar)\nFamiliarity with GPU-accelerated inference optimization and profiling\nExperience with feature stores, data versioning, or ML metadata management\nKnowledge of canary deployment strategies and progressive delivery for ML models\nCompensation Range: $160K - $220K","company":"Deepgram","rawCompany":"deepgram","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-08-05T12:13:13.992Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1244.00","title":"Network and Computer Systems Administrators","slug":"network-and-computer-systems-administrators"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"ML Ops Infrastructure Engineer","description":"Location\nUSA | Remote\nEmployment Type\nFull time\nLocation Type\nRemote\nDepartment\nEngineering\nCompensation\n$160K – $220K • Offers Equity • Offers Bonus\nThis range is determined by work location and additional factors, including job-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.\n\nPlease note that the compensation details listed on US role postings reflect the base salary only and does not include bonus, equity or benefits.\n\nCompany Overview\nDeepgram 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.\nCompany Operating Rhythm\nAt 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.\nEvery 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.\nAdditionally, 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.\nThe Opportunity\nGetting 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.\n\nWhat You'll Do\nDesign and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment\nArchitect and maintain model deployment pipelines that move models from research environments through staging to production with confidence\nBuild A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact\nImplement comprehensive monitoring for model performance in production - accuracy metrics, latency, drift detection, and regression alerts\nDevelop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences\nCreate and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment\nEstablish model versioning, artifact management, and rollback capabilities to ensure safe and reproducible deployments\nCollaborate with research engineers to define and enforce model quality gates before production promotion\nBuild observability dashboards that give the team real-time insight into model health across all environments\nOptimize model serving infrastructure for latency, throughput, and cost efficiency\n\nYou'll Love This Role If You\nAre excited by the challenge of operationalizing cutting-edge AI models at production scale\nBelieve that great infrastructure is what turns research breakthroughs into customer value\nEnjoy designing systems that are automated, reliable, and self-healing\nWant to work on problems where minutes of latency reduction or percentage points of accuracy matter enormously\nLike collaborating across research and engineering teams to make the whole organization faster\nAre motivated by building the deployment and testing systems that back a platform serving over 200,000 developers\n\nIt's Important To Us That You Have\n4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems\nStrong proficiency in Python and experience building automation and tooling for ML workflows\nDeep experience with CI/CD systems and building pipelines for software and model delivery\nHands-on experience with Docker and Kubernetes for containerized workload management\nPractical experience deploying and serving ML models in production environments\nFamiliarity with model evaluation, validation, and quality assurance processes\nUnderstanding of monitoring and observability principles as applied to ML systems\nStrong problem-solving skills and a bias toward automation over manual processes\n\nIt Would Be Great If You Had\nExperience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime\nBackground in speech, audio, or real-time media ML systems\nExperience with Infrastructure as Code tools such as Terraform or Pulumi\nHands-on experience with monitoring and observability stacks (Prometheus, Grafana, Datadog, or similar)\nFamiliarity with GPU-accelerated inference optimization and profiling\nExperience with feature stores, data versioning, or ML metadata management\nKnowledge of canary deployment strategies and progressive delivery for ML models\nCompensation Range: $160K - $220K","datePosted":"2026-08-05T12:13:13.992Z","dateModified":"2026-08-05T12:13:13.992Z","hiringOrganization":{"@type":"Organization","name":"Deepgram","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6f76781f817076fa7bc45d99"},"url":"https://jobsearcher.com/jobs/6f76781f817076fa7bc45d99"}}