Machine Learning Engineer, TTS

Cantina

. Architect, implement, pre-train, fine-tune, and post-train/alignment large-scale speech models .

Posted 10/27/2026full-timeRemoteMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and implementing large-scale speech models, with a strong focus on transformer architectures and distributed training. Proven ability to lead research projects, analyze scientific experiments, and ensure production reliability and observability.

Highest-signal resume keywords

Large Scale Audio ModelsTransformer ArchitecturesMulti-Node Distributed TrainingPyTorchSpeech Control

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Large Scale Audio ModelsTransformer ArchitecturesMulti-Node Distributed TrainingPyTorchCUDAC++Audio Language ModellingVoice CloningSpeech GenerationPerformance Profiling

Soft Skills

CollaborationIndependent Research Leadership

Tools & Technologies

CUDATritonCloud InfrastructureMonitoring ToolsDevelopment Tooling

Industry Keywords

Speech TechnologyMachine LearningData AugmentationModel EvaluationRobustness Checks

Tech Stack

Tools & technologies

CloudNode.jsPyTorchC++

About the role

Key responsibilities & impact

Architect, implement, pre-train, fine-tune, and post-train/alignment large-scale speech models
Independently lead small research projects while collaborating on larger team initiatives
Design, run, and analyze scientific experiments
Develop and improve development tooling
Contribute across the stack, from low-level optimizations to high-level model design
Define data requirements and collaborate on acquisition, curation, augmentation, labeling quality, and synthetic data strategies
Design automated objective and subjective evaluations, including listening tests, SV/WER/ASR-based metrics, robustness and bias checks, and red-team studies
Harden the training-to-evaluation-to-inference pipeline; profile latency, memory, and cost; and meet production SLAs with monitoring and rollback
Partner with infrastructure on distributed training/inference on cloud fleets and productionize models with reliability and observability
Contribute to safety/consent guardrails and misuse/abuse mitigation for responsible speech technology

Requirements

What you’ll need

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, including 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, including 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, and voice generation
Experience with audio language modelling and transformer architectures
No educational credential explicitly required

Benefits

Comp & perks

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, and 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