[All remote jobs](https://jobicy.com/jobs.md)[![Cantina Labs logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/11/e52bb5e84323-221.webp)](https://jobicy.com/company/cantina-labs.md)[Cantina Labs](https://jobicy.com/company/cantina-labs.md) # Machine Learning Engineer, Voice Conversion AI Summary Cantina Labs seeks a senior Research / Machine Learning Engineer to build production-grade voice-conversion and related generative speech systems. The role owns the full model, data, evaluation, and inference lifecycle, including training large audio models, designing listening tests, and optimizing reliability, latency, and cost. Candidates need deep experience with diffusion or flow-matching transformers, neural audio codecs, distributed GPU training, and PyTorch performance engineering. The position also requires close collaboration with research, data, infrastructure, and product teams while implementing safety and misuse-mitigation guardrails. It is a highly specialized hands-on role for someone who has shipped large-scale speech or multimodal models.[Apply for this job](#job-application)[View company](https://jobicy.com/company/cantina-labs.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md), [Europe](https://jobicy.com/job-region/europe.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceOpen levelPublished10 Nov 2026Apply before10 Dec 2026Listing views42Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## 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 VC and adjacent tasks (controllable TTS, voice design 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. You will thrive in this role if you: * See research and engineering as two sides of the same coin and enjoy owning work end-to-end. * Are results-oriented, flexible, and willing to pick up whatever moves the needle. * Like collaborating closely with infra, data, and product to ship measurable improvements. * Enjoy designing experiments, listening tests, and metrics that correlate with user-perceived quality. * Eager to learn every-day, find and solve unique large-scale problems. ### 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. * 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. * 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 (>8B parameters, >500k hours of data). * Deep hands-on experience with diffusion and/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation. * Deep hands-on experience training audio VAEs, neural audio codecs, and vocoders latent/tokenizer design, reconstruction and perceptual objectives, adversarial training. * Strong experience with multi-node, multi-GPU distributed training (FSDP/DeepSpeed or equivalent). * 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 or multimodal generative models to production. * Background in working with large-scale ML data, and the ability to iterate on data and triangulate quality using both subjective and objective signals. * Experience with voice cloning, speech control/steerability, or expressive speech generation. * Notable publications and/or open-source contributions 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! Show more [Apply now >](https://jobicy.com/jobs/160999-machine-learning-engineer-voice-conversion.md) > Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is an advanced research-engineering role requiring demonstrated work on speech models exceeding 8B parameters and 500k hours of data, as well as production deployment expertise. It combines cutting-edge ML research, distributed systems, performance optimization, evaluation science, and responsible-AI design. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$270,000US market range (per year)$220k–$320k0$352k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer base salary was disclosed. Estimated US-market annual base pay for a highly senior speech ML engineer with large-scale generative-audio and production systems expertise is $220,000–$320,000 USD, with an estimated midpoint of $270,000 USD; this is an AI estimate, not employer-provided compensation. ## Core skills Skills and capabilities most closely associated with this opportunity. [Speech machine learning](https://jobicy.com/jobs?search_keywords=Speech%20machine%20learning.md)[Voice conversion](https://jobicy.com/jobs?search_keywords=Voice%20conversion.md)[Text-to-speech](https://jobicy.com/jobs?search_keywords=Text-to-speech.md)[Generative audio models](https://jobicy.com/jobs?search_keywords=Generative%20audio%20models.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[Diffusion transformers](https://jobicy.com/jobs?search_keywords=Diffusion%20transformers.md)[Flow matching](https://jobicy.com/jobs?search_keywords=Flow%20matching.md)[Distributed training](https://jobicy.com/jobs?search_keywords=Distributed%20training.md)[FSDP](https://jobicy.com/jobs?search_keywords=FSDP.md)[DeepSpeed](https://jobicy.com/jobs?search_keywords=DeepSpeed.md)[CUDA optimization](https://jobicy.com/jobs?search_keywords=CUDA%20optimization.md)[Triton](https://jobicy.com/jobs?search_keywords=Triton.md)[Neural audio codecs](https://jobicy.com/jobs?search_keywords=Neural%20audio%20codecs.md)[Model evaluation](https://jobicy.com/jobs?search_keywords=Model%20evaluation.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md) Sample interview questionsDescribe a large-scale speech or audio model you trained and deployed. What were the principal technical challenges?A strong answer should explain the model architecture, data scale, training setup, evaluation methodology, production deployment path, and measured outcomes. It should specifically address issues such as data quality, distributed-training stability, inference latency, model quality, and operational monitoring. How would you evaluate a voice-conversion model beyond a single automatic metric? I would combine objective measures such as speaker-verification similarity, WER or ASR intelligibility, latency, and robustness with controlled human listening tests for naturalness, identity preservation, expressiveness, and artifacts. I would segment results by speaker, language, recording condition, and demographic factors, then use error analysis to prioritize data or modeling changes. What practical decisions matter when training diffusion or flow-matching models for speech generation? Important choices include the audio representation or latent codec, conditioning design, noise or flow schedule, sampler, guidance strategy, sequence length, and distillation approach. I would validate these choices through ablations against perceptual quality, speaker consistency, inference cost, and stability rather than optimizing only training loss. How have you diagnosed and improved multi-node, multi-GPU training performance? I begin with profiling to separate input-pipeline, compute, memory, and communication bottlenecks. Depending on findings, I may tune FSDP or DeepSpeed sharding, mixed precision, activation checkpointing, gradient accumulation, dataloader throughput, kernel efficiency, and network configuration, then verify improvements with reproducible throughput and convergence measurements. What safeguards would you implement for a voice-cloning or voice-conversion product? I would build consent and provenance checks into data and enrollment workflows, apply access controls and abuse detection, maintain audit logs, and establish clear policies for impersonation risks. I would also red-team the system, test for bypasses, provide reporting and response mechanisms, and ensure safeguards are monitored and updated after deployment. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. Keep exploring ## Related remote jobs. * ![Deepgram logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/12/WRILS-201212160008-406229.jpg) Deepgram Nov 10 New ### [Director of Research, Text-to-Speech](https://jobicy.com/jobs/161053-director-of-research-text-to-speech.md) Company Overview Deepgram is bringing the world’s most natural interface, voice, to the technology we use every day. 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