Senior Machine Learning Engineer (Ads)

Location: Bellevue, Washington, United States

Salary: $185,000–$235,000 USD annual base pay

Company

hirify.global is a content intelligence platform serving personalized local news and information to more than 40 million monthly active users through AI, recommendation systems, and advertising technology.

What you will do

• Design, implement, and own batch and streaming feature pipelines that transform raw user events into targeting and bidding features.
• Develop identity prediction, user and content embedding, intent, conversion, and signal-quality models.
• Integrate model outputs directly into ads targeting, bidding, and ranking systems.
• Own the full ML lifecycle, including data preparation, feature engineering, training, deployment, monitoring, evaluation, and iteration.
• Build low-latency online serving across feature stores, embedding stores, and inference infrastructure for real-time bidding.
• Drive technical direction through design reviews, engineering best practices, mentoring, and cross-functional collaboration with Ads, Data, and Platform teams.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
• 5+ years of industry experience building and shipping production ML models as an ML engineer or applied scientist.
• Strong knowledge of feature engineering, supervised learning, embeddings, representation learning, and offline and online evaluation.
• Proficiency in Python and ML libraries such as PyTorch or TensorFlow, scikit-learn, and pandas/NumPy.
• Experience with large-scale data processing and production pipelines using technologies such as Spark, Flink, SQL, Presto, or Trino.
• Experience taking models from development through deployment, monitoring, A/B testing, and optimization based on business metrics.

Nice to have

• Experience in ads, recommendation, search, or growth ML, especially targeting, bidding, ranking, CTR/CVR prediction, or identity prediction.
• Experience with behavioral event pipelines, user embeddings, online feature serving, feature stores, or vector stores.
• Experience with Kafka, Flink, Spark Streaming, distributed model training, inference optimization, or MLOps tooling.
• Demonstrated business impact through revenue, ROAS, CTR, or CVR improvements and technical leadership across teams.

Culture & Benefits

• Ownership of a mission-critical part of the advertising stack with measurable business impact.
• Collaboration with Ads, ML, and Data engineers on internet-scale problems.
• Health, dental, and vision coverage for employees and families, with 100% employee coverage.
• 401(k) plan with company matching, paid time off, and paid holidays.
• FSA, HSA, commuter benefits, and a team activity budget.