Director, ML Research Science (Adtech / Recommender Systems)

The role

We are seeking a technical leader who can balance strategic leadership with hands-on contributions. You’ll oversee a growing team of ML research scientists, guide innovation in deep learning and LLMs, and directly advance Cognitiv’s real-time bidding and recommendation systems. This role is critical to our success, sitting at the intersection of cutting-edge research and production-scale delivery.

Location: This position will be located in San Mateo, CA with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote (Thursday/Friday).

What You'll Do

• Lead and Mentor. You manage and grow a team of Machine Learning Research Scientists, fostering a collaborative, innovative environment while mentoring individuals on both technical challenges and career development.


• Set Strategic Direction. You define and execute the vision for machine learning research within the adtech domain, representing the team in strategic discussions and contributing to company-wide initiatives.


• Drive Technical Innovation. You oversee the design and implementation of cutting-edge deep learning architectures, staying current with LLM research and guiding the integration of new breakthroughs into Cognitiv’s solutions.


• Stay Hands-On. You actively contribute through coding, experimentation, and code reviews, ensuring technical excellence and adherence to best practices.


• Advance AdTech Performance. You continuously improve models and algorithms to drive ad targeting, real-time bidding performance, and audience relevance.


• Enable Scalable Systems. You collaborate with operations, engineering, and cross-functional partners to refine data pipelines, model deployment, and monitoring systems.


• Deliver Results. You manage project timelines, resources, and deliverables, ensuring successful completion of high-impact research initiatives.

Tech Stack

• Core Tools – Python, PyTorch, deep learning architectures (transformers, recommendation models).


• Traditional ML – XGBoost, PCA.


• Big Data / Infra – Spark, Hadoop, distributed training systems.


• Cloud Platforms – AWS, GCP, or Azure.


• Bonus – C++.

Who You Are

• Experienced Leader with Advanced Education: 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. Proven experience leading teams of researchers and senior ICs/PhDs while remaining 30–50% hands-on (coding, reviews, experimentation).


• Deep Learning, LLMs & Model Tuning: Deep technical expertise in PyTorch, transformers, and Large Language Models (LLMs), including large-scale training and fine-tuning of deep neural networks.


• Machine Learning Breadth: 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.


• Engineering Excellence: 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).


• Production Experience: 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 Communicator: Excellent written and verbal communication skills, strong project management capabilities, and the ability to drive alignment in fast-paced, dynamic environments.

Bonus Points If You Have

• AdTech & RTB Experience. Prior exposure to advertising technology and real-time bidding (RTB) systems is a strong plus.


• Distributed Systems & Cloud. Familiarity with big data frameworks (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).


• C++ Skills. Strong C++ programming ability is a significant advantage alongside Python expertise.


• Research & Community Impact. A track record of published research or meaningful contributions to the machine learning community.


• Bridging Research and Delivery. Experience managing both exploratory research timelines and production-grade delivery cycles.

Salary: $250,000 - $330,000 USD Base Salary + Equity

What We Offer