Senior Machine Learning Engineer

Senior Machine Learning Engineer – Data Enablement

Who are we?

Auction Technology Group (ATG) is transforming the multi-billion-dollar global auction industry. Our platforms connect thousands of auction houses with buyers in over 170 countries, powering more than $15 billion in annual sales. Through innovative online auction technologies, we help auctioneers expand their reach, boost efficiency, and maximize value—while giving bidders unrivaled access to rare and specialized items. As a publicly traded company, ATG has scaled from $18 million to $170 million in revenue, with sustained growth beyond the pandemic. We're modernizing one of the last industries to fully go digital—building a global, category-defining business in the process.

Who are we looking for?

We are making a significant investment in creating a user experience that meets the expectations of our customers. Not only do you put the customer at the heart of everything you do, but you are adept at enabling data-driven decisions to design and deliver strategic projects. You will be comfortable working cross-functionally with Product, Engineering, MLOps, and Analytics teams to develop our products and improve the end user experience. You should have a strong track record of successful prioritization, meeting critical deadlines and enthusiastically tackling challenges with an eye toward problem solving.

What your contributions will be:

• Design and develop state-of-the-art recommendation algorithms leveraging collaborative filtering, content-based filtering, and hybrid approaches to surface relevant auction items to bidders
• Build and optimize learning-to-rank models that re-rank search results and recommendations based on user preferences, behavioral signals, and contextual features
• Develop personalization systems that adapt to individual user interests, browsing patterns, and bidding history across multiple auction categories and marketplaces
• Build classification and embedding models to better represent our product taxonomy and enable semantic similarity matching across diverse auction items
• Collaborate closely with the engineering and MLOps teams to integrate machine learning algorithms into production systems and APIs
• Perform rigorous experimentation (A/B testing) to demonstrate the causal impact of recommendation strategies and conduct analyses to identify challenges and opportunities, deriving valuable insights
• Leverage computer vision techniques to enhance visual similarity recommendations and improve content understanding
• Stay updated with scientific advancements in recommender systems, personalization, and ranking, and contribute to technical publications when possible

What you need for Success:

Educational Background:

• MSc or PhD in relevant fields such as Machine Learning, Data Science, Computer Science, Statistics, or related disciplines

Required Skills:

• Strong expertise in Python and familiarity with data science and machine learning libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch
• Solid understanding of recommendation system architectures: collaborative filtering (matrix factorization, neural collaborative filtering), content-based filtering, and hybrid approaches
• Experience with learning-to-rank algorithms (e.g., pointwise, pairwise, and listwise approaches such as RankNet, LambdaMART, LambdaRank) and their application to re-ranking problems
• Proficient in deep learning techniques for recommendations, including neural networks, embeddings, two-tower models, and transformer-based architectures
• Understanding of personalization techniques: user profiling, behavioral modeling, contextual bandits, and online learning
• Experience with evaluation metrics for recommender systems (e.g., Precision@K, Recall@K, NDCG, MRR, diversity metrics, coverage)
• Familiarity with handling sparse data, cold-start problems, and implicit feedback signals
• Knowledge of feature engineering for recommendation systems, including user features, item features, and interaction features
• Understanding of A/B testing frameworks and experimental design for measuring recommendation quality

Nice-to-Have:

• Experience with large-scale embedding systems and vector databases (e.g., Elastic, Milvus, Pinecone)
• Familiarity with computer vision models for visual similarity and image-based recommendations
• Knowledge of multi-armed bandit algorithms and exploration-exploitation strategies
• Experience with session-based or sequence-aware recommendation models (e.g., RNNs, transformers for sequential recommendations)
• Understanding of fairness, diversity, and serendipity in recommendation systems
• Experience with marketplace or e-commerce recommendation systems

Soft Skills:

• Ability to conduct practical research with a scientific mindset and a focus on delivering actionable results
• Strong communication and interpersonal skills, with a proven ability to work collaboratively in a team-oriented environment
• Excellent problem-solving skills, capable of abstracting complex problems into their essential components and developing effective solutions
• Ability to balance technical excellence with business impact and user experience considerations