Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4355302872&f_TPR=r10000&f_WT=2&keywords=software%20engineer&origin=JOB_SEARCH_PAGE_JOB_FILTER&start=75 Job Description: ATG (Auction Technology Group) Share Show more options Machine Learning Engineer  United States · 23 minutes ago · 47 people clicked apply Promoted by hirer · Responses managed off LinkedIn Remote Matches your job preferences, workplace type is Remote. Full-time Matches your job preferences, job type is Full-time. Apply Save Save Machine Learning Engineer  at ATG (Auction Technology Group) Your profile was shared with the job poster. Undo shared profile with the job posterUndo Did you apply? Let us know, and we’ll help you track your application. Yes No Machine Learning Engineer ATG (Auction Technology Group) · United States (Remote) Apply Save Save Machine Learning Engineer  at ATG (Auction Technology Group) Show more options Your profile is missing required qualifications Show match details Help me update my profile BETA Is this information helpful? Get personalized tips to stand out to hirers Find jobs where you’re a top applicant and tailor your resume with the help of AI. Try Premium for PKR0 Meet the hiring team Elizabeth Ostrander 3rd Human Resources Generalist II Job poster Message About the job 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. 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 Key Requirements 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 Featured benefits