Machine Learning Engineer

About the Role

As a Machine Learning Engineer at Dandelion Labs, you worked at the intersection of research and production. You took cutting-edge ML techniques and turned them into reliable, scalable systems that powered our clients' products. This wasn't just about training models in notebooks — it was about building the full pipeline from data ingestion to model serving, ensuring everything runs smoothly at scale. You collaborated closely with data scientists, backend engineers, and product teams to deliver ML-powered features that made a real difference for startups trying to stand out in competitive markets.

Responsibilities

• Design, build, and deploy machine learning models for production environments
• Develop and maintain ML pipelines for data processing, feature engineering, and model training
• Optimize model performance for latency, throughput, and resource efficiency
• Collaborate with data scientists to translate research prototypes into production-ready systems
• Implement monitoring and alerting for model performance and data drift
• Build APIs and services that expose ML capabilities to other teams and products
• Document architectures, decisions, and best practices for the team
• Mentor junior engineers and contribute to team knowledge sharing

Requirements

• 4+ years of experience in software engineering with at least 2 years focused on ML systems
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or similar)
• Experience deploying models to production using Docker, Kubernetes, or cloud-native solutions
• Solid understanding of ML fundamentals: supervised/unsupervised learning, model evaluation, feature engineering
• Experience with data processing tools (Spark, Airflow, or similar)
• Familiarity with cloud platforms (AWS, GCP, or Azure) and their ML services
• Strong software engineering practices: version control, testing, code review, CI/CD
• Excellent communication skills and ability to explain technical concepts to non-technical stakeholders

Nice to Have

• Experience with MLOps tools (MLflow, Kubeflow, or similar)
• Background in deep learning, NLP, or computer vision
• Contributions to open-source ML projects
• Experience working in early-stage startups
• Publications or conference presentations in ML/AI