Senior Machine Learning Engineer

### Who you are
- Master’s or Ph.D. in Computer Science, Machine Learning, AI, or a related field
- 5+ years of hands-on experience building, evaluating, and deploying ML models in production
- Strong background in speech recognition (ASR), speech processing, or closely related domains
- Deep experience with model evaluation, benchmarking, and error analysis for ML systems
- Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face)
- Solid understanding of modern ML techniques, including transformer-based models and large-scale training
- Experience building data pipelines and tooling for large-scale experimentation and quality analysis
- Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements

### What the job involves
- A key focus of this role is advancing model evaluation, measurement, and quality improvements, with particular emphasis on Automatic Speech Recognition (ASR) and downstream NLP systems
- You will design rigorous evaluation frameworks, define quality metrics, and drive systematic improvements to model accuracy, robustness, and reliability
- You will work closely with applied researchers, product teams, and platform engineers to ensure that model performance improvements translate into measurable business impact
- As a Senior Machine Learning Engineer, you will be at the forefront of applying modern ML and speech/NLP techniques to production systems
- Your work will focus on improving ASR quality, building scalable evaluation and benchmarking infrastructure, and enabling continuous model iteration through data-driven insights
- Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems
- Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration
- Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements
- Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation
- Partner with applied research to translate experimental improvements into production-ready systems
- Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals
- Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale
- Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production

### Benefits
- Work with an A+ team of ex-Founders, world leading scientists, and nationally competitive athletes
- Weekly lunch & learn
- Mentorship - A world class mentorship board advises and mentors our team-members to capture the opportunity ahead of us