Machine Learning Engineer

Summary

Join a venture-scale company in the PropTech industry as a Senior Full-stack Engineer and be a foundational member, owning key technical systems from day one. Work remotely, collaborating during EST hours, in a focused environment dedicated to AI/ML development, research, and delivery. The role involves training and evaluating ML models, developing NLP pipelines, performing fine-tuning and prompt engineering for LLMs, and creating semantic search and recommendation models. You will collaborate with software engineers, prepare documentation, and contribute to a culture built on speed, iteration, and execution. This position offers professional development opportunities, mentorship from startup veterans, and a flexible work setup.

Requirements

• Strong proficiency in Python for machine learning and data processing
• Experience with NLP libraries: spaCy, Hugging Face Transformers, gensim, nltk
• Comfortable training deep learning models using Keras, TensorFlow, or PyTorch
• Ability to design and execute ML experiments, evaluate models, and interpret results
• Familiar with version control (Git), shell scripting, and Linux development environments
• Basic back end software engineering skills, such as creating and managing endpoints, database services, and task queues
• Experience with production environments (e.g., batch inference, model packaging)

Responsibilities

• Train and evaluate ML models using common machine learning frameworks in Python. Examples include TensorFlow, Keras, scikit-learn, or PyTorch
• Develop and refine NLP pipelines (e.g., tokenization, entity recognition, similarity models)
• Perform fine-tuning and prompt engineering for LLMs (GPT, Claude, etc.)
• Create semantic search and recommendation models using vector embeddings and clustering techniques
• Conduct experiments, hyperparameter tuning, and performance benchmarking
• Collaborate with software engineers to integrate models into backend systems
• Prepare clear documentation, model cards, and evaluation reports

Preferred Qualifications

• Experience with MLOps tools (e.g., MLflow, SageMaker, DVC)
• Contributions to Kaggle competitions, AI research, or open-source ML/NLP projects
• Background in classical ML, unsupervised learning, or semantic modeling

Benefits

• Professional Development : Annual learning budget for books, courses, and conferences
• Mentorship : Learn directly from startup veterans (ex-Looker, GitHub, Mulesoft)
• Inspiring Workspaces : Offices in Berlin, New York, and London, with travel opportunities
• Flat Hierarchy : Work directly with founders and have your ideas heard
• Flexible Work Setup : Equipment of your choice, strong home office support