Staff Machine Learning Engineer

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🕒 July 17

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Billy Goat Group

WebsiteLinkedInAll Job Openings

1 - 10 employees

💼 Consulting

📦 Logistics

Consulting • Logistics • Advertising

Billy Goat Group is a company specializing in targeted advertising services. They offer solutions to place advertisements on the mobile phones of potential customers who visit competitors or other strategic locations. Billy Goat Group boasts quick and cost-effective advertising solutions compared to traditional PPC or SEO methods, focusing on direct access to desired clients and efficient tracking from ad view to business entry. Their expertise spans various industries, including automotive repair, home builders, pet food stores, gyms, and more, ensuring exclusive campaigns for top businesses without conflicts of interest.

📋 Description

• Own the full lifecycle of predictive models in production — architecture, training pipelines, inference infrastructure, deployment, and ongoing model health • Build and operate the systems that route model outputs into live product surfaces: search ranking, recommendations, feed ordering, and related user-facing experiences • Establish and maintain model monitoring, alerting, drift detection, and retraining cadences — the feedback loops that keep deployed models accurate over time • Partner closely with Data Science, Data Engineering, Product Management, and backend engineering to move work from validated approach to production system • Own the decision-making process on whether to leverage ML infrastructure & expertise from our parent company, GOAT Group, and when to advocate for building in-house solutions. • Contribute to ML infrastructure decisions — serving architecture, feature computation, pipeline orchestration — with an eye toward what scales as the team and model count grows • Set technical standards and raise the bar for how ML systems are built, evaluated, and operated across the pod

🎯 Requirements

• 7+ years of engineering experience, with substantial depth in production machine learning systems. • Demonstrated end-to-end ownership: training pipelines through deployed inference, not just modeling. • Advanced knowledge of ML, AI and statistical models, as well their application in e-commerce settings. • Strong proficiency in Python; SQL; DBT; airflow or similar. • Solid software engineering fundamentals. • Experience with ranking, retrieval, or recommendation systems. • Demonstrated expertise with ML lifecycle tooling — experiment tracking, model versioning, pipeline orchestration, drift detection — and comfort working with modern data infrastructure (cloud warehouse, search/retrieval systems).

🏖️ Benefits

• 401K • paid time off • dental • medical • vision • disability • life insurance options