The OpportunityOur Audio team is building frontier speech-language models that handle STT, TTS, and speech-to-speech in a single architecture. This role sits at the center of applied audio model development, working directly with the technical lead to ship production systems that run on-device under real-time constraints. You will own critical workstreams across data pipelines, evaluation systems, and customer deployments. If you want high ownership on rare technical problems in a small, elite team where your code ships, this is the role.
What We're Looking ForWe need someone who:
- Builds first, theorizes later: You ship working systems, not just notebooks. Production-grade code is your default, not a stretch goal.
- Owns outcomes end-to-end: From data pipelines to customer deployments, you take responsibility for the full stack without waiting for someone else to handle the hard parts.
- Thrives under constraints: On-device, low-latency, memory-limited systems excite you. You see constraints as design parameters, not blockers.
- Ramps quickly on new territory: Gaps in specific subdomains are fine if you close them fast. You seek out feedback and stay focused on what moves the needle.
The Work- Build and scale data pipelines for audio model training, including preprocessing, augmentation, and quality filtering at scale
- Design, implement, and maintain evaluation systems that measure multimodal performance across internal and public benchmarks
- Fine-tune and adapt audio models for customer-specific use cases, owning delivery from requirements through deployment
- Contribute production code to the core audio repository, collaborating with infrastructure and research teams
- Support experimentation under real hardware constraints, shifting between customer work and core development as priorities evolve
Desired ExperienceMust-have:- Strong programming fundamentals with demonstrated ability to write clean, maintainable, production-grade code
- Experience building and shipping production ML systems beyond model training (data pipelines, evals, serving infrastructure)
- Proficiency in PyTorch and familiarity with distributed training frameworks (DeepSpeed, FSDP, or similar)
- Track record of collaborating effectively in shared codebases with high engineering standards
Nice-to-have:- Direct experience with audio/speech models (ASR, TTS, vocoders, diarization, or speech-to-speech systems)
- Experience designing and running large-scale training experiments on distributed GPU clusters
- Open-source contributions that demonstrate code quality and engineering judgment
What Success Looks Like (Year One)- Within 6 months, you independently deliver production-ready data pipelines or evaluation systems and own at least one customer workstream end-to-end
- Your PRs to the core audio repo are accepted without heavy rework, demonstrating strong judgment in system design
- By year end, you operate as a second pillar to the technical lead, unblocking parallel workstreams and raising overall team velocity
What We Offer- Rare technical problems: Work on audio-to-audio frontier systems with real ownership in a team small enough that your contributions ship directly to production.
- Compensation: Competitive base salary with equity in a unicorn-stage company
- Health: We pay 100% of medical, dental, and vision premiums for employees and dependents
- Financial: 401(k) matching up to 4% of base pay
- Time Off: Unlimited PTO plus company-wide Refill Days throughout the year
