Machine Learning Engineer
Core
Building AI models for human-like conversations at the intersection of speech, language, and intelligence to power real-time, scalable systems.
Role type
Machine Learning Engineer (AI Infrastructure)
Builds
Scalable ML systems, end-to-end ML pipelines, and MLOps infrastructure for voice and language AI.
Domain
AI Infrastructure / Speech & Language Technology
Deliverable
production ML models
Required skills
Machine learning, deep learning, NLP, MLOps, data pipelines, Python, Go, ML lifecycle management tools, system design for robustness and scalability, data engineering
Preferred skills
Speech recognition, TTS, audio processing, LLMs, generative AI, real-time inference, data orchestration frameworks, cloud infrastructure, containerization
Technologies
MLflow, Kubeflow, Weights & Biases, Airflow, Prefect, Dagster, AWS/GCP/Azure, Docker, Kubernetes
Responsibilities
Design, build, and maintain scalable ML systems from data ingestion to deployment; Develop and optimize end-to-end ML pipelines; Implement robust MLOps practices including model versioning and CI/CD; Collaborate with product and engineering teams to integrate models into real-time products; Ensure data quality, observability, and performance across AI systems; Stay current with AI infrastructure and research.
Seniority
Early-stage hire, hands-on IC
