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# Senior Machine Learning Engineer, Ads Response Prediction

- **Company:** The Ladders
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $180,000.0 - $190,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Data Transformation, Information Retrieval, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Pytorch, Apache Spark, Deep Learning, Pandas
- **Published:** September 21, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/plrfawpijn

## About the Role

* Master's or PhD in ML, statistics, CS, information retrieval, or a related quantitative field, or equivalent experience
 * 3+ years of combined academic and industry experience applying ML to ranking, recommendation, or prediction problems
 * Deep understanding of CTR/conversion prediction modeling and various ML architectures
 * Strong foundation in causal inference and bias mitigation techniques
 * Proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX, as well as data manipulation tools such as SQL, Spark, and Pandas
 * Experience formulating complex ML research directions and conducting rigorous experiments
 * Strong communication skills for explaining modeling decisions to cross-functional teams

## Description

This role will focus on advancing advertising prediction systems that improve relevance, calibration, and decision quality across multiple surfaces. You will own research and development efforts that shape how prediction models learn from user behavior and support better outcomes for the business. The position partners closely with applied scientists and cross-functional stakeholders to translate ambiguous modeling challenges into rigorous research directions., * Own research and development of pCTR and conversion prediction models to enhance model calibration and reduce training data biases

 * Design and implement debiasing techniques such as Mixed Negative Sampling and Inverse Propensity Weighting to address prediction biases
 * Contribute to next-generation multi-domain, multi-task model architecture with advanced methodologies such as Mixture-of-Experts and Transformer layers
 * Expand sequence modeling efforts for generative retrieval and semantic ID representation across various ad surfaces
 * Collaborate with the ML community on foundation models for autoregressive user behavior prediction
 * Translate ambiguous modeling problems into defined ML research directions with evaluation criteria
 * Publish and share findings to foster a culture of technical rigor within the team


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