> Markdown version of [/jobs/ext/3047835-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3047835-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Happyrobot Inc. - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Audio Signal Processing, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, Software Debugging, Python (Programming Language), Machine Learning, Natural Language Processing, Speech Recognition, AI Infrastructure, Data Ingestion, Large Language Models, Deep Learning, Generative AI, Containerization, Kubernetes, Machine Learning Operations, Software Version Control, Data Pipelines, Docker - **Published:** September 24, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + 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. Nice to Have + 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). ## Description 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. ## Related Videos - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Microservices: how to get started with Spring Boot and Kubernetes](https://www.wearedevelopers.com/videos/242-microservices-how-to-get-started-with-spring-boot-and-kubernetes) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)