Staff Research Scientist, AdTech Product Innovation

Cognitiv; San Mateo (CA); Hybrid; USD 200,000 - 270,000; Full time

Benefits offered by this job

Medical/Dental/Vision

Parental leave

PTO & WFH

Equity

Hybrid work model

Wellness stipend

401(k) match

Parking

Employee assistance

Cognitiv onboarding

Job summary

Cognitiv is seeking a Staff Research Scientist to advance deep learning and LLM research, contributing to real-time bidding and recommendation systems at production scale in Bellevue, WA with a hybrid schedule.

You will design, prototype, and deploy cutting-edge ML models, collaborating with data science and engineering teams to solve complex ML challenges and stay at the forefront of transformer research.

Qualifications

• Master’s or Ph.D. in Computer Science, Statistics, Electrical Engineering, or a related field.
• Strong expertise in PyTorch, transformers, and LLMs.
• Experience with real-time, high-throughput ML systems.

Responsibilities

• Design, prototype, and evaluate advanced ML approaches for recommendation and RTB.
• Contribute to production-grade ML systems and deployment.
• Collaborate with data science, engineering, and product teams to solve complex ML challenges.

Skills

Python

PyTorch

Transformers

LLMs

Recommender systems

XGBoost

PCA

Spark

Hadoop

C++

Deep learning

Education

Master’s or Ph.D. in Computer Science/Statistics/Electrical Engineering or related

Tools

AWS

GCP

Azure

Distributed training systems

Job description

Are you ready to revolutionize the advertising industry?

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.

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.

Now, we’re growing!

The Role

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 Bellevue, WA office with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote (Thursday/Friday).

What You'll Do

• 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.

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 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.

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.

Salary: $200,000 - $270,000 USD Base Salary + Equity

What We Offer

Compensation is based on experience, skills, and other factors. Base salary is just one part of your total rewards at Cognitiv—you’ll also receive equity and a comprehensive benefits package.

Highlights include:

• 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)
• …and more!

What You’ll Find at Cognitiv

• Festiv – We make work fun with cross‑team games, events, and creative team bonding.
• Responsiv – You’ll be close to clients and leadership, influencing real outcomes.
• Inclusiv – Diversity and individuality are celebrated across all levels.
• Inventiv – We reward curiosity and embrace bold ideas.
• Transformativ – We support your growth with training, mentorship, and flexibility.
• Collaborativ – We operate across coasts, connected by purpose and teamwork.

Cognitiv is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive workplace for all.

Note on AI Use: Cognitiv may use AI technology to assist with certain administrative aspects of the hiring process, such as note‑taking, interview documentation, and reporting. However, every resume and application is reviewed directly by our recruiting team. AI tools are used solely for operational support and do not influence candidate evaluation or hiring decisions.