Algorithm Engineer

Responsibilities:

1. Formulate and solve optimization problems for cross-channel budget pacing and intelligent bid adjustments across major search platforms.
2. Perform complex exploratory data analysis (EDA) to evaluate the financial impact of strategic initiatives and the performance of various creative combinations.
3. Utilize and fine-tune Large Language Models (LLMs) to generate and iterate high-converting ad copy and brainstorm keywords at scale.
4. Integrate multi-modal/image generation models to produce and optimize visual assets for ads and landing pages.
5. Implement algorithms to logically organize massive, unstructured keyword lists into coherent ad groups.
6. Partner closely with data and software engineers to deploy bidding rules, analytical models, and Generative AI pipelines into robust production environments.

Job Requirements:

1. Master‘s degree or PhD in Computer Science, Data Science, Statistics, Operations Research, AI/Machine Learning, or a highly quantitative field.
2. 2+ years of experience in algorithmic roles, data science, or machine learning, ideally with exposure to both optimization/analytics and NLP/Generative AI.
3. Strong programming skills in Python and deep proficiency with SQL for complex data extraction, manipulation, and analysis.
4. Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and working knowledge of LLMs (e.g., prompt engineering, fine-tuning).
5. Deep understanding of statistical concepts, causal inference, and rigorous A/B testing design.
6. Exceptional analytical problem-solving skills and the ability to adapt to changing business needs across different technical domains.

Preferred skills and qualifications

• Direct experience in AdTech, computational advertising, dynamic creative optimization (DCO), or marketing science.
• Background in Operations Research, bidding strategies, or budget allocation algorithms.
• Experience with text-to-image models (e.g., Stable Diffusion) or multi-modal AI applications.
• Familiarity with vector databases and RAG (Retrieval-Augmented Generation) architectures.