Machine Learning Engineer

HappyRobot is the infrastructure for enterprises to build and orchestrate AI workforces. Our AI workers don't just communicate - they make decisions, take action, and run operations autonomously across voice, email, and enterprise systems. Born in Y Combinator (S23) and backed by a16z and Base10 with over $60M raised, we power critical operations for global enterprises worldwide.

Our platform is battle-tested in the most demanding environments - where AI has real consequences. We started in logistics, built our own voice stack, models, and orchestration layer from the ground up, and are now bringing that infrastructure to every enterprise that runs the real economy. Learn more about our vision in our manifesto.

You’ll be building AI models that make human-like conversations possible. You’ll work at the intersection of speech, language, and intelligence, taking cutting-edge research and transforming it into real-time, scalable systems that power our core products. You’ll have the unique opportunity to make a huge impact as one of our first ML hires, shaping not only the technology but also the direction of our company. From designing robust models to deploying them in production, you’ll own the entire lifecycle of ML systems and help us stay ahead of the curve in AI innovation.

• Design, build, and maintain scalable ML systems — from data ingestion and preprocessing to training, testing, and deployment.

• Develop and optimize end-to-end ML pipelines (data collection, labeling, training, validation, monitoring) to ensure reliability and reproducibility.

• Implement robust MLOps practices, including model versioning, experiment tracking, CI/CD for ML, and continuous monitoring in production.

• Collaborate with product and engineering teams to integrate and deploy models into real-time products with a focus on efficiency and scalability.

• Ensure data quality, observability, and performance across all AI systems.

• Stay current with the latest in AI infrastructure, tooling, and research — helping us stay ahead of the curve.

• Strong experience in machine learning, deep learning, and NLP.

• Solid background in MLOps and data pipelines — e.g., model deployment, monitoring, and scaling in production environments.

• Proficiency in Python and familiarity with Go.

• Experience with ML lifecycle management tools (e.g., MLflow, Kubeflow, Weights & Biases).

• Ability to design ML systems for robustness, scalability, and automation.

• Strong coding, debugging, and data engineering skills.

• Passion for AI infrastructure and its real-world impact.

• Founder mindset: ownership, independence, and willingness to go deep.

• Experience in speech recognition, TTS, or audio processing.

• Familiarity with LLMs, generative AI, or real-time inference systems.

• Hands-on experience with data orchestration frameworks (e.g., Airflow, Prefect, Dagster).

• Prior experience in startup environments with fast iteration cycles.

• Knowledge of cloud infrastructure (AWS/GCP/Azure) and containerization tools (Docker, Kubernetes).