Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Machine Learning Engineer, TTS
- Salary
- Undisclosed
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 10 Dec 2026
- Listing views
- 36
- Application actions
- 2
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About this role.
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:
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!
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
I would combine objective measures such as WER through ASR, speaker-verification similarity, latency, and failure rates with structured human listening tests for naturalness, intelligibility, prosody, and speaker similarity. I would segment results by language, accent, input type, and difficult edge cases, then use dashboards and regression gates to prevent quality or safety regressions before release.
I would first profile memory, communication, and input-pipeline bottlenecks, then select an appropriate parallelism strategy such as FSDP, tensor parallelism, or pipeline parallelism. I would use mixed precision, checkpointing, efficient data sharding, fault-tolerant checkpoints, and monitoring for throughput, loss stability, GPU utilization, and communication overhead.
I start with explicit product quality and latency targets, profile the full serving path, and identify the highest-cost model components. Depending on findings, I may apply distillation, quantization, streaming generation, batching, caching, kernel optimization, or architecture changes, validating each trade-off with both objective metrics and listening evaluations.
I would build consent verification and provenance controls into the data and enrollment flows, restrict cloning of protected or unverified voices, and maintain audit logs and abuse-reporting processes. I would also red-team impersonation risks, monitor for suspicious usage patterns, establish clear escalation paths, and continuously evaluate whether safeguards are effective.
In a strong example, I would compare model failures across data slices and use listening tests plus objective metrics to identify gaps such as noisy labels, insufficient speaker diversity, or weak coverage of difficult phonetic contexts. I would then prioritize targeted curation, augmentation, relabeling, or synthetic-data generation, run controlled retraining experiments, and retain only interventions that improve the relevant quality and robustness measures.
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