Senior Machine Learning Engineer, Features (Adtech)
Cognitiv
San Mateo, CA · Posted July 21, 2026
Just laid off? Start here: a plan for the first weeks
See every open role at Cognitiv
More in California job market · All Senior Machine Learning Engineer jobs (209) · Technology & Software · Data & Analytics jobs
More roles at Cognitiv
- Senior Product Manager, Custom Algorithms · New York, NY
- Senior Software Engineer, Backend · Bellevue, WA
- Senior Site Reliability Engineer, AWS/Datacenter Hybrid · Bellevue, WA
- Finance Analyst, AI/Automation · New York, NY
- ML Data Infrastructure Engineer · San Mateo, CA
Similar jobs
- Senior Platform Engineer · Baker Tilly · CA
- Principal Platform Engineer - Integrations · Baker Tilly · CA
- Specialist Systems Engineer · L3HARRIS Technologies · Carlsbad, CA
- Lead Systems Engineer · L3HARRIS Technologies · Yorba Linda, CA
- Sr. IT Systems Engineer- Palmdale, CA- Secret Required · Sikorsky Aircraft · Palmdale, CA
- Senior Solutions Architect, First Time Deployment Validation - NVIS · Nvidia · Santa Clara, CA
Where San Mateo, CA ranks
Where San Mateo, CA placed in our monthly rankings, month by month. Each month stays online at its own address.
November 2026
- #1Best cities for engineering pay after cost of living$216k · of 94
- #1Best cities for finance pay after cost of living$162k · of 35
- #1Best cities for HR pay after cost of living$158k · of 15
- #1Best cities for legal pay after cost of living$238k · of 20
- #1Best cities for security pay after cost of living$241k · of 10
- #1Best cities for media and entertainment pay after cost of living$209k · of 9
Rankings about data & analytics jobs
- Best cities for data and analytics jobs #1 New York (1,713 openings)
- Best cities for data and analytics pay after cost of living #1 Foster ($232k)
- Most requested AI skills in data and analytics jobs #1 Machine learning engineering (17.1%)
- AI tools named most often in data and analytics jobs #1 Databricks (7.6%)
- Employers with the most data and analytics jobs #1 Amazon (711 roles)
Data & Analytics jobs · Top Data & Analytics employers · All rankings
Pay compared with the market
This listing does not state pay. Across 76 open senior machine learning engineer listings that state pay, the middle half runs $195,000 to $256,000 and the median is $217,500.
Listed pay is what employers advertise, not what people are paid.
Senior Machine Learning Engineer salaries by state · Check your own pay
How AI is used in this kind of work
Among computer occupations, all other, AI is already used for a large share of the tasks. Measured exposure for computer occupations, all other: 31% (high).
Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.
Skills this job asks for
Say which you have and which you want to build. We use it to match you to jobs like this one.
About the job
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!
In this role, you will work at the intersection of large-scale distributed systems, machine learning engineering, and platform architecture. You will own and evolve critical data capabilities within Cognitiv’s advertising ecosystem, with a primary focus on designing and delivering stable, highly scalable feature pipelines, as well as optimizing the performance and reliability of ad model training pipelines.
This is a senior individual-contributor role for an engineer who thrives on solving complex platform problems, raising the technical bar, and building systems that serve many teams at scale. You will partner closely with engineers, product managers, data scientists, infrastructure teams, and downstream data consumers to deliver platforms that are resilient, extensible, and easy to adopt.
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 optional (Thursday/Friday).
What You'll Do
High-Performance Feature Engineering & Infrastructure: Architect, build, and maintain low-latency, high-throughput feature pipelines — batch, real-time streaming, and point-in-time correct historical features — to power our real-time bidding systems.
Advanced Embeddings & Generative AI Integration: Leverage LLMs and deep learning models to extract rich contextual and user-level embeddings into the core feature store/serving system, optimizing embedding generation, indexing, and online retrieval for sub-millisecond serving SLAs.
Model Training Pipeline Optimization: Own and continuously enhance Cognitiv's ad model training pipelines, improving training speed, resource utilization, and throughput for large-scale deep learning models.
System Scalability, Reliability & Efficiency: Establish technical standards for monitoring, testing, and CI/CD across feature and training infrastructure to ensure robust system SLAs/SLOs.
Partner with Modeling & Data Science: Translate complex signals into production-ready features that directly boost model performance (e.g., CTR/CVR prediction).
Who You Are
Must haves:
Strong Fundamentals / First Principles Thinking
3-5+ years of hands-on Machine Learning Infrastructure / Data Platform experience supporting data-intensive platforms, including large-scale data pipelines, streaming systems, and storage layers.
Proficiency in one or more core programming languages — Python, Java, or Scala — for building, maintaining, and scaling robust ML and data pipelines.
Domain expertise in AdTech.
Strong expertise in modern big data technologies such as Apache Spark, Apache Flink, Apache Kafka, and other distributed data processing frameworks.
Excellent team communication, cross-functional collaboration, and problem-solving skills, with a track record of partnering effectively with modeling and engineering teams.
Nice to haves:
Domain expertise in AdTech
Hands-on experience with PyTorch for model architecture, training pipeline acceleration, or distributed training.
Proficiency with cloud & infrastructure technologies, including AWS/GCP, as well as containerization and orchestration platforms like Kubernetes (K8s) and Docker.
Competitive programming background, such as awards or achievements in OI (Olympiad in Informatics) or ACM/ICPC.
Tech Stack: Python, Java, Kafka, Flink, Spark, PyTorch, Kubernetes, AWS
What Success Looks Like in Your First 30/60/90 Days
First 30 days:
Ramp quickly on Cognitiv's feature pipelines, training infrastructure, and RTB systems.
Build strong context on current architecture, data flows, and known bottlenecks.
Establish relationships with Modeling, Data Science, and Infrastructure teams.
Identify early opportunities to improve pipeline reliability, training efficiency, or serving latency.
First 60 days:
Independently own at least one core feature pipeline, embedding service, or training subsystem.
Ship a first measurable improvement — e.g., reduced serving latency, faster training throughput, or expanded monitoring/CI-CD coverage.
Align with Modeling and Data Science on the feature and training roadmap.
Begin influencing technical direction for feature and training infrastructure.
First 90 days:
Fully own a core area of the platform, such as embedding generation/retrieval, streaming infrastructure, or training pipeline performance.
Deliver measurable business impact — e.g., reduced feature or serving latency, faster model training, or improved uptime/SLA adherence.
Redesign or scale at least one key system to handle growing data volume or model complexity.
Operate autonomously as a trusted technical partner to Modeling, Data Science, and Engineering.
Salary: $190,000-$250,000 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.