Top AI & Machine Learning Jobs in San Francisco, CA

Research Engineer responsible for identifying weaknesses in voice AI models, defining improvement targets, designing evaluations, curating and generating data, fine-tuning deep learning models, and rapidly shipping production systems. The role emphasizes end-to-end ownership of customer problems, measurable production improvements, practical judgment, and fast experimentation. Candidates should have strong Python and PyTorch skills, production ML experience, and ideally experience with speech technologies, distributed training, publications, open source, or public benchmarks.

Research and improve voice AI models by identifying production weaknesses, defining evaluation metrics, curating data, fine-tuning deep learning systems, and rapidly shipping solutions. The role focuses on speech-to-text, text-to-speech, voice agents, model evaluation, customer-driven research, and production machine learning systems.

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.

Research Staff will develop foundational voice AI technologies, including low-bitrate neural audio codecs, steerable speech generation, disentangled audio representations, latent recombination, synthetic audio data generation, and multimodal speech-to-speech models. The role also involves designing scalable architectures, training methods, and inference algorithms optimized for hardware, billion-hour datasets, and real-time deployment. Candidates need strong mathematical foundations, foundation-model expertise, large-scale data pipeline experience, rigorous experimentation skills, deployment optimization knowledge, and publications or open-source contributions in speech or language AI.

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.

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.

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.

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 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.