Staff Research Scientist (AdTech/Recommendation Systems)

Cognitiv•San Mateo, CA

1d•$200,000 - $300,000•Hybrid

About The Position

At Cognitiv, we are industry trailblazers redefining media buying with our Deep Learning Advertising Platform. Since 2015, we have harnessed the power of cutting-edge deep learning technology and data science to transform how brands connect with their customers. Our mission is to bring intelligence to advertising and deliver unparalleled precision, relevance, and impact at scale. With our innovative platform, advertisers enjoy unprecedented flexibility—whether it is activating Dynamic Deals through their preferred DSP, leveraging our managed service DSP, or utilizing our industry-first ContextGPT product. As a part of Cognitiv, you will be at the forefront of AI-driven advertising solutions, driving change and achieving remarkable growth in a rapidly evolving industry. We are seeking a Staff Research Scientist who can drive innovation through deep technical expertise and hands-on execution. You’ll contribute to cutting-edge research in deep learning and LLMs while advancing Cognitiv’s real-time bidding and recommendation systems at production scale. This role sits at the intersection of applied research and high-performance machine learning systems.

Requirements

Master’s or Ph.D. in Computer Science, Statistics, Electrical Engineering, or a related field, with 5–7+ years of experience in machine learning R&D or applied ML.
Strong technical expertise in PyTorch, transformers, and Large Language Models (LLMs), including large-scale training, fine-tuning, and optimization of deep neural networks.
Strong understanding of both deep learning and traditional ML techniques (e.g., XGBoost, PCA), with the ability to apply the right approach to the right problem.
Proficiency in Python with strong foundations in algorithms, data structures, and software engineering principles; experience building models in real-time, high-throughput systems (e.g., recommender systems, adtech).
Hands-on experience developing, deploying, and optimizing machine learning models in production environments, including distributed systems, cloud platforms (AWS, GCP, Azure), and big data frameworks (Hadoop, Spark).
Strong written and verbal communication skills with the ability to work effectively across research and engineering teams in a fast-paced environment.

Nice To Haves

Prior exposure to advertising technology and real-time bidding (RTB) systems is a strong plus.
Familiarity with big data frameworks (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
Strong C++ programming ability is a significant advantage alongside Python expertise.
A track record of published research or meaningful contributions to the machine learning community.
Experience translating research ideas into scalable, production-grade machine learning systems.

Responsibilities

Drive Research & Innovation: Design, prototype, and evaluate advanced machine learning and deep learning approaches, with a focus on recommendation systems, real-time bidding, and LLM-driven applications.
Stay Hands-On: Contribute directly through coding, experimentation, model development, and technical problem-solving across the full ML lifecycle.
Advance AdTech Performance: Improve model accuracy, scalability, and efficiency to drive ad targeting, bidding performance, and audience relevance.
Build Production-Ready ML Systems: Partner closely with engineering and infrastructure teams to deploy, optimize, and monitor machine learning models in large-scale production environments.
Explore Emerging Technologies: Stay current with advancements in deep learning, transformers, and LLM research, identifying practical opportunities to apply new techniques within Cognitiv’s platform.
Collaborate Cross-Functionally: Work closely with data science, engineering, product, and platform teams to solve complex technical challenges and deliver impactful ML solutions.
Contribute Technical Expertise: Provide thoughtful technical input through design discussions, experimentation reviews, and collaboration with other researchers and engineers.