Job Overview
Location
USA | Remote
Job Type
Full-time
Category
Software Engineering
Date Posted
July 10, 2026
Full Job Description
📋 Description
- • 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
🎯 Requirements
- • 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
🏖️ Benefits
- • Competitive salary
- • Comprehensive benefits package
- • Flexible work arrangements
- • Opportunities for professional growth and development
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Deepgram Inc.
Deepgram builds end-to-end speech AI infrastructure that converts live or recorded audio into text and insights. The company trains large-scale neural networks on GPU clusters to deliver low-latency transcription, keyword detection, and speaker diarization through a single API. Developers use the platform for call centers, meetings, podcasts, and voice bots, paying per minute or hosting the engine on-premise. Founded in 2015 and headquartered in San Francisco, Deepgram serves enterprises seeking accurate, private, and customizable speech recognition without vendor lock-in.
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