Staff Research Scientist (AdTech/Recommendation Systems)

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.

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).

• 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.

• 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++.

• Experienced ML Researcher/Engineer: 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.
• Deep Learning & LLM Expertise: Strong technical expertise in PyTorch, transformers, and Large Language Models (LLMs), including large-scale training, fine-tuning, and optimization 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).
• Collaborative Communicator: Strong written and verbal communication skills with the ability to work effectively across research and engineering teams in a fast-paced environment.

1. AdTech & RTB Experience. Prior exposure to advertising technology and real-time bidding (RTB) systems is a strong plus.
2. Distributed Systems & Cloud. Familiarity with big data frameworks (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
3. C++ Skills. Strong C++ programming ability is a significant advantage alongside Python expertise.
4. Research & Community Impact. A track record of published research or meaningful contributions to the machine learning community.
5. Bridging Research and Production. Experience translating research ideas into scalable, production-grade machine learning systems.

Salary: $200,000 - $300,000 USD Base Salary + Equity