Machine Learning Engineer, TTS

Cantina Labs
Greater London, UK
7 days ago
Apply on www.collegerecruiter.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
£200,000.0 - £220,000.0
Working hours
Regular working hours

Tech stack

Artificial Intelligence C++ (Programming Language) Profiling Nvidia CUDA Data Governance Machine Learning Node.Js Open Source Technology Software Engineering Data Streaming Pytorch Data Strategy
+1 more
Machine Learning Operations

Job description

About Cantina

Cantina is a new social platform founded by Sean Parker with the most advanced AI character creator. Our bots are lifelike, social creatures that can interact wherever people are online-across voice, video, and text. Create yourself, imagine someone new, or choose from thousands of characters to share infinitely scalable, personalized content and seamless group chat.

If you’re excited about how AI can shape creativity and social interaction, come help us build what’s next.

About The Role

We’re looking for a Research / ML Engineer to join our Speech Team to build state-of-the-art speech systems end-to-end-from data specs through production inference. You’ll drive the model data eval flywheel for TTS and adjacent tasks (voice cloning, controllable TTS, voice conversion and more), partnering closely with research, data, and infra to ship fast, reliable, and cost-aware models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

What You’ll Do

  • Model Building: Architect, implement, pre-train, fine-tune, and post-train/alignment (e.g., GRPO/DPO) for large-scale speech models.
  • Project Leadership: Independently lead small research projects while collaborating on larger team initiatives.
  • Experimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models.
  • Tool Development: Develop and improve dev tooling to enhance team productivity.
  • Full-Stack Contribution: Contribute to the entire stack, from low-level optimizations to high-level model design.
  • Data Ownership: Define data requirements and collaborate on acquisition, curation, augmentation, labeling quality, and synthetic data strategies.
  • Rigorous Evaluation: Design automated objective/subjective evaluations-listening tests, SV/WER/ASR-based metrics, robustness & bias checks, and red-team studies.
  • Pipeline Delivery: Harden the training * evaluation * inference pipeline; profile latency, memory, and cost; and meet production SLAs with robust monitoring and rollback.
  • GPU Scaling: Partner with infrastructure to run distributed training/inference on cloud fleets and productionize models with reliability and observability.
  • Safety & Responsibility: Contribute to safety/consent guardrails and to misuse/abuse mitigation for responsible speech technology.

What You’ll Bring

  • Exceptional research/development experience with large scale audio models (>3B models and >500k hours data).
  • Exceptional understanding and hands-on experience with transformer architectures and/or diffusion models (inc. distillation and streaming) and/or audio language modelling.
  • Strong experience with multi-node and multi-gpu distributed model training.
  • Strong software engineering skills with a proven track record of building complex systems
  • Strong with PyTorch and performance work (profiling, CUDA/Triton/C++ as needed) and writing reliable production quality code.
  • Shipped large scale speech/audio models to production.
  • Background in working with large-scale ML data.
  • Ability to iterate on data,, and triangulate quality using subjective and objective signals.
  • Notable publications and/or open source contributions in speech/audio/ML.
  • Experience with voice-cloning, speech-control, voice-generation.

Preferred Experience

  • Shipped large scale speech/audio models (TTS/VC/ASR) to production.
  • Work on large-scale ML systems.
  • Experience with audio language modelling, transformer architectures.
  • Experience with voice-cloning, speech-control, voice-generation.
  • Background in processing large-scale ML data.
  • Publications or notable open-source in speech/audio/ML.

Compensation

The anticipated annual base salary range for this role is between $200,000-$220,000 (€170,000-€190,000). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.

Benefits For U.S.-based Roles

  • Competitive salary and generous company equity
  • Medical, dental, and vision insurance - 99.99% of premiums covered by Cantina
  • 42 days of paid time off, including:

  • 15 PTO days
  • 10 sick days
  • 15 company holidays
  • 2 floating holidays

Generous parental leave & fertility support

401(k) retirement savings plan

Lifestyle spending account - $500/month to use however you’d like

Complimentary lunch and snacks for in-office employees

One Medical membership, and more!

Requirements

  • Exceptional research/development experience with large scale audio models (>3B models and >500k hours data).
  • Exceptional understanding and hands-on experience with transformer architectures and/or diffusion models (inc. distillation and streaming) and/or audio language modelling.
  • Strong experience with multi-node and multi-gpu distributed model training.
  • Strong software engineering skills with a proven track record of building complex systems
  • Strong with PyTorch and performance work (profiling, CUDA/Triton/C++ as needed) and writing reliable production quality code.
  • Shipped large scale speech/audio models to production.
  • Background in working with large-scale ML data.
  • Ability to iterate on data,, and triangulate quality using subjective and objective signals.
  • Notable publications and/or open source contributions in speech/audio/ML.
  • Experience with voice-cloning, speech-control, voice-generation.

Preferred Experience

  • Shipped large scale speech/audio models (TTS/VC/ASR) to production.
  • Work on large-scale ML systems.
  • Experience with audio language modelling, transformer architectures.
  • Experience with voice-cloning, speech-control, voice-generation.
  • Background in processing large-scale ML data.
  • Publications or notable open-source in speech/audio/ML.

Benefits & conditions

The anticipated annual base salary range for this role is between $200,000-$220,000 (€170,000-€190,000). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data., * Competitive salary and generous company equity

  • Medical, dental, and vision insurance - 99.99% of premiums covered by Cantina
  • 42 days of paid time off, including:

  • 15 PTO days
  • 10 sick days
  • 15 company holidays
  • 2 floating holidays

Generous parental leave & fertility support

401(k) retirement savings plan

Lifestyle spending account - $500/month to use however you’d like

Complimentary lunch and snacks for in-office employees

About the company

About Cantina

Cantina is a new social platform founded by Sean Parker with the most advanced AI character creator. Our bots are lifelike, social creatures that can interact wherever people are online-across voice, video, and text. Create yourself, imagine someone new, or choose from thousands of characters to share infinitely scalable, personalized content and seamless group chat.

If you’re excited about how AI can shape creativity and social interaction, come help us build what’s next.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.collegerecruiter.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

45 sec

Working securely with Node.js path application programming interfaces

Sonya Moisset · World Congress 2023

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · World Congress 2023

47 sec

Profiling native execution calls with async-profiler

Gonzalo Ortiz Jaureguizar Gonzalo Ortiz Jaureguizar · World Congress 2026 Europe

2:00 min

Speaker background and current technology implementations

Manjuri Sinha Manjuri Sinha · World Congress 2025

3:55 min

Identifying underlying Node.js runtime vulnerabilities using fuzzing tools

Sonya Moisset · World Congress 2023

2:26 min

Capabilities and applications of large language models

Aditi Godbole · LIVE

Videos

See all

Related articles

See all