Staff Research Scientist (AdTech/Recommendation Systems)

About the role

At Cognitiv, we are not just another AdTech company—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? To bring intelligence to advertising and deliver unparalleled precision, relevance, and impact at scale. Now, we’re growing!

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.

Requirements

• 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.

Qualifications

• 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 Production. Experience translating research ideas into scalable, production-grade machine learning systems.

Skills

• 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++.

Benefits

• Medical, Dental and Vision plan for US employees & Extended Health Benefits for Canadian employees
• 12 weeks paid parental leave + 4 weeks WFH
• Unlimited PTO + Work-From-Anywhere August
• Career development with clear advancement paths
• Equity for all employees
• Hybrid work model & daily team lunch
• Health & wellness stipend + cell phone reimbursement
• 401(k) & RRSP with employer match
• Parking (CA, WA, Vancouver offices) & pre-tax commuter benefits
• Employee Assistance Program
• Comprehensive onboarding (Cognitiv University)

Pay

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

Schedule

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