ATG (Auction Technology Group) · 14 hours ago
Machine Learning Engineer
United States
Full-time
Remote
Mid, Senior LevelAuction Technology Group (ATG) is transforming the global auction industry with innovative online auction technologies. They are seeking a Machine Learning Engineer to design and develop advanced recommendation algorithms, collaborate with cross-functional teams, and enhance user experience through data-driven solutions.
AuctionsE-CommerceOnline PortalsSocial MediaSoftware
Hiring Manager
Elizabeth Ostrander
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
Qualification
Required
MSc or PhD in relevant fields such as Machine Learning, Data Science, Computer Science, Statistics, or related disciplines
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
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
Preferred
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
Company
ATG (Auction Technology Group)
A global technology company with over 350 employees, ATG is transforming the way billions of pounds, euros and dollars worth of items are bought and sold globally via auction.
Founded in 1971
201-500 employees
Funding
Current Stage
Public CompanyTotal Funding
$272.96MKey Investors
Mobeus Equity Partners
2025-02-17Post Ipo Debt· $159M
2024-12-12Post Ipo Secondary· $106.99M
2021-02-23IPO
Leadership Team
John-Paul Savant
CEO
Lesa Musatto
Chief Marketing Officer
Recent News
talent4boards.com
2025-03-24
Company data provided by crunchbase