ML Ops Infrastructure Engineer

Job not on LinkedIn

🕒 April 6

🇺🇸 United States – Remote

💵 $160k - $220k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷 Infrastructure Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 32%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Deepgram

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2015

💼 Consulting

🏥 Healthcare

📦 Logistics

💰 $47M Series B on 2022-11

Consulting • Healthcare • Logistics

Deepgram is a leading voice AI company that provides powerful APIs for speech-to-text, text-to-speech, and language understanding applications. Their platform enables developers to build sophisticated voice AI solutions for use cases such as contact centers, medical transcription, conversational AI, and more. Known for unmatched accuracy, speed, and cost-effectiveness, Deepgram's technology is trusted by top enterprises and startups worldwide. By offering real-time and highly accurate transcription capabilities, Deepgram helps businesses gain insights from voice data, making it an essential tool for transforming voice interactions.

📋 Description

• 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

🎯 Requirements

• 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

🏖️ Benefits

• Medical, dental, vision benefits • Annual wellness stipend • Mental health support • Life, STD, LTD Income Insurance Plans • Unlimited PTO • Generous paid parental leave • Flexible schedule • 12 Paid US company holidays • Quarterly personal productivity stipend • One-time stipend for home office upgrades • 401(k) plan with company match • Tax Savings Programs • Learning / Education stipend • Participation in talks and conferences • Employee Resource Groups • AI enablement workshops / sessions