About the Role
The role involves developing and optimizing machine learning systems for speech processing, integrating research advancements into production-grade platforms, and improving efficiency and accuracy across large-scale models and data pipelines.
Responsibilities
- Design and implement machine learning models tailored for speech recognition tasks
- Optimize training pipelines for speed, scalability, and resource efficiency
- Collaborate with researchers to transition prototypes into deployable systems
- Improve inference performance across diverse hardware environments
- Develop tools for monitoring and evaluating model behavior in production
- Work closely with infrastructure teams to streamline deployment workflows
- Identify bottlenecks in data processing and model training workflows
- Contribute to the design of distributed training frameworks
- Ensure models maintain high accuracy under real-world conditions
- Support versioning and reproducibility of machine learning experiments
- Integrate new algorithmic approaches into existing model architectures
- Analyze model outputs to detect biases or performance degradation
- Build automated testing frameworks for model updates
- Maintain documentation for system components and workflows
- Participate in code reviews and technical design discussions
- Stay current with advancements in machine learning and speech systems
- Contribute to open-source projects when applicable
- Troubleshoot issues across the machine learning lifecycle
- Collaborate on defining evaluation metrics for new capabilities
- Assist in benchmarking against industry standards
- Work with data scientists to refine labeling pipelines
- Ensure compliance with data privacy and security standards
- Optimize model size and latency for edge deployment scenarios
- Support continuous integration and delivery of ML components
- Engage in long-term planning for system evolution
Nice to Have
- PhD or advanced degree in computer science or related field
- Prior work in speech processing or audio machine learning
- Contributions to open-source machine learning projects
- Experience with model compression or quantization
- Knowledge of hardware acceleration for inference
- Familiarity with WebAssembly or edge runtime environments
- Published research in machine learning or systems conferences
- Experience with large language models or self-supervised learning
- Background in real-time signal processing
- Involvement in benchmarking or standardized evaluations
Benefits
- Comprehensive health insurance coverage
- Dental and vision plans
- 401(k) retirement savings plan
- Flexible paid time off policy
- Parental leave benefits
- Mental health and wellness support
- Home office stipend
- Professional development budget
- Stock options or equity participation
- Life and disability insurance
Compensation
Competitive salary and equity package
Work Arrangement
Remote
Team
Small, fast-moving team focused on advancing core speech technology
Our Mission
We are building highly accurate, efficient speech recognition systems that understand real-world audio with minimal latency and high scalability.
Engineering Culture
- We value clear technical communication, ownership of projects, and iterative improvement through data-driven decisions.
- Engineers are expected to balance innovation with maintainability and long-term system health.
Available for qualified candidates
