ML Engineer

Design, build, and deploy production-grade ML systems with end-to-end ownership. The role focuses on AI-powered solutions for natural speech interaction and real-time audio understanding, using cutting-edge AI to optimize field sales teams' customer interactions.

• Design, build, and deploy production-grade ML systems with end-to-end ownership of the model lifecycle.
• Architect and deliver AI-powered solutions for natural speech interaction and real-time audio understanding.
• Develop and optimize ML models focused on audio data to extract business-critical insights.
• Build agents capable of operating natively on real-world audio inputs.
• Collaborate with cross-functional teams to shape AI stack and drive innovation in LLM and audio ML applications.
• Work directly with customers to identify needs and deliver impactful solutions.
• Handle the entire AI lifecycle: data acquisition, preprocessing, model training, deployment, inference, and monitoring.
• Participate in continuous improvement of ML infrastructure and processes.

• Bachelor's or Master's in Computer Science, Machine Learning, AI, or related field.
• 1-6 years of professional experience in ML engineering.
• Strong programming skills in Python (TypeScript is a plus).
• Hands-on experience with ML frameworks like PyTorch or TensorFlow.
• Familiarity with cloud environments and infrastructure (preferably AWS).
• Strong understanding of data pipeline design, real-time inference, and model monitoring.
• Excellent communication skills for customer and stakeholder engagement.
• Proven experience building and deploying ML models into production.
• Demonstrated ability to own the full model lifecycle.
• Experience with audio-focused ML projects or similar unstructured data domains.
• Proficiency in building scalable data pipelines for model training and evaluation.

• FastAPI, OpenAI APIs, Baseten, LiteLLM, LiveKit, PostgreSQL, Redis, and S3 experience.
• Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices.