Top AI & Machine Learning Jobs in Austin, TX

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.

Lead and build Deepgram's AI enablement function: prototype and ship reusable agents, MCP servers, paved-road workflows, prompt and retrieval patterns, and an enablement hub. Drive company-wide AI adoption with measurable productivity metrics, embed safe-use guardrails, partner across Platform, Security, Data, and People Ops, and grow a central team and distributed champions network.

Lead end-to-end delivery of ML infrastructure and AI tooling programs, defining architecture and rollout strategies for training, serving, and monitoring. Coordinate research, engineering, product, and data teams to optimize inference cost/latency, build internal tools for experiment tracking and model versioning, and resolve bottlenecks to enable faster, scalable ML deployments.

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.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.