Machine Learning Engineer, Speech LLM Training - San Francisco

Plaud, San Francisco, CA

Overview

ABOUT PLAUD INC.

Plaud is building the real-world AI interface for professionals to amplify intelligence, elevate productivity and performance, loved by over 3,000,000 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. With GDPR, SOC 2 Type II, HIPAA, and EN 18031 compliance, Plaud is committed to the highest standards of data security and privacy protection.

To learn more about Plaud, please visit and follow along on Instagram , X , Facebook , LinkedIn , and YouTube .

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Machine Learning Engineer, Speech LLM Training - San Francisco

Plaud, San Francisco, CA

Posted pay

$195K–$365K

a year

Compared with similar jobs

This base pay is above the median for ML and AI engineering jobs in the Bay Area with no level in the job title.

Median: $220K a year
Other jobs in the group: 696

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location: San Francisco, CA
Work type: Hybrid
Level: Not given
Skills in the job text: PyTorch
JAX
Posted: May 8, 2026
Last check: 4 h ago
Job type: ML / AI engineering, Inference & ML systems

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Plaud

Open tech jobs: 28
With posted pay: 54%
Median posted base: $145K–$219K

About Plaud: funding, hiring and pay

Plaud

About Plaud: funding, hiring and pay

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Overview

ABOUT PLAUD INC.

To learn more about Plaud, please visit and follow along on Instagram , X , Facebook , LinkedIn , and YouTube .

What you need

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.

Strong candidates may also have experience with

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.

Machine Learning Engineer, Speech LLM Training - San Francisco

Overview

Machine Learning Engineer, Speech LLM Training - San Francisco

Overview

What you need

Strong candidates may also have experience with

Why You Should Join Us

Pay and benefits

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Interview questions

Plaud

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