Top AI & Machine Learning Jobs

Research Staff will develop foundational voice AI technologies, including neural audio codecs, steerable generative models, latent-space embeddings, synthetic audio data generation, multimodal speech-to-speech systems, and hardware-efficient architectures. The role combines mathematical modeling, algorithmic innovation, large-scale data engineering, real-time deployment optimization, and rigorous experimental design to advance scalable, low-latency voice interaction.

Conduct foundational research on latent space models for voice AI, including neural audio codecs, generative speech, interpretable embeddings, synthetic data generation, multimodal speech-to-speech systems, and hardware-efficient model architectures. Design mathematical formulations, scalable data pipelines, controlled experiments, and real-time inference solutions for large-scale audio datasets.

Conduct foundational research on latent space models for voice AI, including neural audio codecs, generative speech models, interpretable embeddings, synthetic audio generation, multimodal speech-to-speech systems, and hardware-efficient training and inference. The role requires mathematical rigor, algorithmic innovation, scalable data pipelines, systems optimization, and controlled experimentation to advance speech and audio foundation models.

Leads Deepgram’s end-to-end Text-to-Speech research program, owning research strategy, technical direction, model development, evaluation, and production deployment. Advances neural audio modeling, prosody, expressiveness, multilingual generation, voice consistency, controllability, and inference performance. Builds and develops research teams, manages technical leaders, prioritizes experiments and compute, evaluates model quality, and partners with engineering and product leadership to ship production-grade models.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Own and grow the self-serve developer funnel from first touch to paid customer. Define and ship product changes across onboarding, console, and activation. Build instrumentation, experimentation, and business rhythms; run rapid hypotheses, measure outcomes, and collaborate with engineering, design, dev rel, marketing, sales, and analytics.

Own Deepgram’s end-to-end text-to-speech research program, including technical strategy, model architectures, experiments, training, evaluation, and production deployment. Lead researchers and technical managers, establish roadmaps, advance naturalness, expressiveness, multilingual generation, controllability, voice consistency, and inference efficiency, and partner with engineering and product leadership to ship high-quality models.

Design and implement scalable, secure inference services for speech processing. Optimize networking, audio transcoding, latency, memory, and distributed compute orchestration. Debug complex system issues, customize backend services for customers, and partner with Product to deliver end-to-end features.

Own technical and strategic partner relationships for infrastructure platforms (silicon, cloud, edge, confidential computing). Lead onboarding, POCs, demos, deployment guidance, troubleshooting, and joint go-to-market to drive adoption and expansion of Deepgram’s voice AI on partner hardware and platforms.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.