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# Senior Software Engineer, Machine Learning (Ads)

- **Company:** Mid-south Testing, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Big Data, Distributed Systems, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Pytorch, Large Language Models, Deep Learning, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_b420543e4dc7491567b45e4357b4ec67

## About the Role

* 5+ years of experience as a Machine Learning Engineer or Data Scientist.
 * 3+ years of experience specifically in Ads ML (ads ranking, personalization, optimization, privacy-compliant user modeling, targeting, or measurement).
 * Strong proficiency in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
 * Experience with applied deep learning (e.g transformers, embedding models).
 * Proven track record of designing, implementing, and scaling ML-driven ad systems in real-world applications.
 * Experience working with real-time ML inference, A/B testing, and optimization frameworks.
 * Experience translating ML evaluation results and performance metrics into actionable product roadmap items.
 * Ability to connect business objectives to ML solutions, with the flexibility to shift focus toward the highest-impact problems as priorities evolve., * Strong understanding of performance advertising and how ML impacts revenue and advertiser retention.
 * Knowledge of ad tech industry standards and ads ecosystem including targeting, retrieval, ranking, pacing, frequency, auction, etc.
 * Experience with large-scale recommendation systems.
 * Experience with large-scale data infrastructure and distributed computing

## Description

* Design, develop, and deploy machine learning models for ads targeting and ranking.
 * Develop sophisticated ML solutions such as identity graph to enhance ad targeting.
 * Build and optimize ad ranking models to serve the most effective ads based on campaign objectives (e.g., app installs, link click).
 * Improve ads targeting and ranking by leveraging both on-platform and off-platform signals.
 * Collaborate cross-functionally with product, engineering, and business teams to define and execute on the Ads ML roadmap.
 * Scale our ML infrastructure to support an increasing number of concurrent ad campaigns while ensuring low-latency decision-making.
 * Drive research and implementation of state-of-the-art ML techniques in the field of online advertising.