Machine Learning Engineer

Auction Technology Group is expanding its team in Guadalajara, Mexico We are looking for exceptional engineering talent to join the team and help us transform the Auction industry.

What will you bring to the team?

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.

Key Responsibilities

• 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., , , 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.