Machine Learning Engineer, Speech LLM Training

This role is part of the Jobright TNT—a private hiring network connecting top talent with leading AI startups such as Perplexity, Mercor, Cresta, Suno, and 150+ others. Only select, high-signal candidates are invited and recommended directly to hiring teams.

About the Company

Plaud Inc. is a hardware and software company building AI-powered voice recorders for note-taking, with over 1 million users globally.

Why Join Us

Compensation range: $200K/yr - $540K/yr.

Responsibilities

• Build and train large-scale audio or speech models from the ground up, including unified SpeechLLMs, advanced ASR, expressive TTS, or generative audio architectures.
• Work at the intersection of research and engineering, designing novel sequence modeling architectures and debugging distributed training clusters.
• Traverse the entire stack—from fundamental signal processing and raw acoustic representations to massive foundation model training and edge-device optimization.
• Demonstrate deep expertise in PyTorch or JAX, optimizing large-scale distributed training runs, managing GPU memory utilization, and resolving complex performance bottlenecks.
• Thrive in a fast-paced, high-growth startup environment, taking extreme ownership of ambiguous problems and driving them into production.
• Build AI systems that natively understand and generate speech, creating a hardware-software AI companion to amplify human productivity.

Requirements

• Proven track record of building and training large-scale audio or speech models from the ground up, including unified SpeechLLMs, advanced ASR, expressive TTS, or generative audio architectures.
• Experience working at the intersection of research and engineering, designing novel sequence modeling architectures and debugging distributed training clusters.
• Comfort traversing the entire stack—from fundamental signal processing and raw acoustic representations to massive foundation model training and edge-device optimization.
• Deep expertise in PyTorch or JAX, with experience optimizing large-scale distributed training runs, managing GPU memory utilization, and resolving complex performance bottlenecks.
• Ability to thrive in a fast-paced, high-growth startup environment, taking extreme ownership of ambiguous problems and driving them into production.
• Passion for building AI systems that natively understand and generate speech, ultimately creating a hardware-software AI companion.

Preferred Qualifications

• 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 (e.g., RLHF or GRPO) to improve conversational cadence, steerability, and alignment in foundation models.
• Deep System Optimization: End-to-end inference and performance optimization using 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.