Top AI & Machine Learning Jobs

Lead architecture and implementation of Deepgram STT, TTS, and voice-agent deployments across APAC. Own post-sales technical success, Tier-3 escalation, performance tuning, and production migrations. Design reference architectures, build reusable samples and automation, drive root-cause analysis, contribute to pre-sales POCs, and partner with Product/Engineering. Produce runbooks and documentation, participate in on-call rotations, and travel regionally (10–20%).

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Own end-to-end deployment of Deepgram's voice AI solutions for restaurant customers: scope, build, and ship AI agents; manage enterprise stakeholders; monitor live performance; drive adoption and expansion; translate field insights into product requirements and ROI metrics.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

The role involves researching and developing large language models (LLMs) with a focus on transformer architecture, data curation, distributed training, and optimization. Responsibilities include conducting experiments, collaborating with teams, and staying updated on deep learning advancements.

Own the end-to-end product experience for AI coding agents integrating Deepgram: discovery, integration/onboarding, production use, verification, and feedback systems. Build measurement, experimentation, and optimization systems; define agent-specific developer platform requirements and prototype changes; turn agent traffic into rapid product improvements and run cross-functional execution.

As a Data Scientist at Deepgram, you'll tackle complex audio data challenges, develop scalable data pipelines, and collaborate with teams to advance voice AI technologies.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

Lead end-to-end design and execution of voice data programs: build ingestion pipelines, labeling specs, tooling, and quality safeguards; align data strategy with research and product; manage vendors and teams; track throughput, quality, and vendor performance; and prototype/deploy data tools to scale datasets that improve speech and voice AI model outcomes.