Machine Learning Engineer II
Build and deploy production ML models for adMarketplace's native search advertising platform — relevance, ranking, CTR/CVR prediction, and bidding — owning the full ML lifecycle with MLOps, low-latency serving, and A/B testing. Core stack: Python, SQL, Spark/Hadoop, TensorFlow/PyTorch, cloud infrastructure.
- Compensation
- $145K–$175K / yr
- Location
- New York, US
- Work setup
- Not specified
- Seniority
- Middle
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 3+ years industry experience in AI/ML, developing and deploying production-grade ML systems
- 2+ years building AI/ML models in ads, relevance, ranking, recommendation systems, or search
- 3+ years building distributed, low-latency, high-throughput batch and online ML services
- 3+ years deploying and maintaining ML pipelines in production, including feature engineering and model monitoring
- 2+ years Python; proficiency with distributed frameworks (Spark, Hadoop), SQL, and cloud infrastructure
- 2+ years with ML packages such as TensorFlow or PyTorch, scikit-learn, and Spark ML
- Ability to operate efficiently in a high-paced, multi-functional, rapidly evolving environment
Nice to have
- 3+ years building ML models for ads/search/recommenders: CTR/CVR prediction, ad selection, keyword bidding, Learning to Rank
- 2+ years building and deploying online experimentation frameworks at scale
- 2+ years building ad selection frameworks using reinforcement learning or contextual bandits
- Experience fine-tuning LLMs
- 1+ year building products using Generative AI powered autonomous agents
Full description
Who We Are
adMarketplace is the largest global marketplace for Native Search advertising. For over 25 years, we’ve connected brands with consumers at the moment they express commercial intent beyond legacy search engines.
Today, our Native Search solutions are exclusively integrated across next-generation platforms where discovery happens, including web browsers, Buy Now Pay Later apps, and AI chat surfaces. Powered by vector search technology, our platform delivers relevant text and product ads that match consumers with the brands, products, and offers they’re actively seeking.
Our award-winning culture is grounded in the principles of Curiosity, Collaboration, Creative Conflict, Commitment, and Competitiveness – our 5Cs. These values drive us to turn complexity into clarity and create measurable value for consumers, advertisers, and publishers alike.
The Role
We are seeking an experienced Senior Machine Learning Engineer passionate about building impactful products in the search and advertising technology ecosystem. As part of our established AI/ML and Search organization, you will be instrumental in developing and optimizing advanced models and applications to enhance our ultra-low-latency ad-serving platform and consumer-facing search solutions.
You will collaborate closely with product, business, and other engineering teams, making significant contributions to strategic initiatives such as relevancy & yield optimization, predictive modeling, and improved bidding performance. You will have a clear career progression path and numerous opportunities for both personal and professional growth in an intellectually stimulating and dynamic work environment.
What You’ll Do:
- Drive end-to-end lifecycle management of AI/ML projects from concept and data acquisition to prototyping, model development, deployment, optimization and ongoing maintenance.
- Implement and champion best practices in MLOps, including data collection, model training pipelines, model deployments, monitoring, alerting, and QA to ensure model reliability and performance.
- Contribute significantly to model architecture decisions, leveraging state-of-the-art machine learning, deep learning, and reinforcement learning techniques.
- Develop and deploy robust feature engineering pipelines and ML services optimized for low latency and high throughput.
- Establish and utilize robust A/B testing and experimentation frameworks to evaluate and iteratively improve model performance.
- Design, build, and maintain production software systems and microservices that support AI/ML applications, with a focus on reliability, scalability, low latency, and high throughput.
- Communicate effectively, collaborate, and build long-term relationships across the organization.