Machine Learning Engineer, Speech LLM Training - San Francisco

Plaud is building the real-world AI interface for professionals to amplify intelligence, elevate productivity and performance, loved by over 2,500,000 users worldwide since 2023. With a mission to amplify human intelligence, Plaud captures, structures, and compounds the intelligence generated in conversations — so humans can think better, decide faster, and execute with clarity.

Plaud Inc. is a Delaware-incorporated, San Francisco-based company pushing the boundary of human–AI intelligence through a hardware–software combination. With full ISO 27001, ISO 27701, SOC 2, GDPR, EN18031, and HIPAA compliances, Plaud is committed to the highest standards of data security and privacy protection.

To learn more about Plaud, please visit https://www.Plaud.ai and follow along on Instagram, X, Facebook, LinkedIn, and YouTube

Plaud is building the next generation intelligence infrastructure and interfaces to capture, extract, and utilize intelligence from what people say, hear, see, and think.

• Plaud is a bootstrapped, skyrocketing, profitable company with a $300M revenue run rate achieved in just three years.

• Define the next-gen paradigm for human-AI interaction.

• Gain exposure to cutting-edge AI for Pro tools and play a direct role in our global expansion.

• Work with passionate teammates who value innovation, collaboration, and customer success.

• Grow your career in a culture that champions continuous learning and fast career development.

• Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere.

• Have a proven track record of building and training large-scale audio or speech models from the ground up, whether that involves unified SpeechLLMs, advanced ASR, expressive TTS, or generative audio architectures.

• Love living at the intersection of research and engineering, eager to design novel sequence modeling architectures one day and debug distributed training clusters the next.

• Are highly comfortable traversing the entire stack—from fundamental signal processing and raw acoustic representations to massive foundation model training and edge-device optimization.

• Possess deep expertise in PyTorch or JAX, with battle scars from optimizing large-scale distributed training runs, managing GPU memory utilization, and resolving complex performance bottlenecks.

• Thrive in a fast-paced, high-growth startup environment where you are expected to take extreme ownership of ambiguous problems and drive them directly into production.

• Are obsessed with building AI systems that natively understand and generate speech, ultimately creating a hardware-software AI companion that amplifies human productivity.

• Text-based LLMs: Hands-on experience with core text-based Large Language Model pretraining, instruction tuning, or RLHF.

• Neural Audio Codecs: Hands-on experience designing and training state-of-the-art neural audio codecs for streamable, high-fidelity audio.

• Generative Architectures: Designing and training diffusion models, flow matching, or autoregressive architectures specifically for speech and voice generation.

• Alignment & Steerability: Applying Reinforcement Learning (RL) techniques (like RLHF or GRPO) to improve conversational cadence, steerability, and alignment in foundation models.

• Deep System Optimization: End-to-end inference and performance optimization, leveraging high-throughput serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang) to minimize latency for real-time cloud streaming.

• Large-Scale Infrastructure: Managing massive GPU clusters, utilizing advanced distributed training frameworks (e.g., FSDP, DeepSpeed), and navigating orchestration tools like Kubernetes.