AI​/ML Full Stack Engineer; Mid Level

Who We Are:

Winning today isn’t about spending more. It’s about knowing more. Understanding more. Seeing more. And acting sooner than everyone else.

Mogul & Co is The Media Intelligence Company—a combination of predictive technology, advanced analytics, and high‑performance media acquisition designed to answer one question with clarity:

How does every $1 in media create the next $10 in revenue and business growth?

We are entering a pivotal phase, expanding rapidly with strong revenue growth. Our leadership integrates two worlds:

• Silicon Valley hyper‑growth operators with experience building frontier AI, automation, and data platforms.
• Fortune 500 and agency executives who have managed global P&Ls, overseen billions in media investment, and led major transactions.

This blend shapes how we operate: focused, precise, data‑driven, and oriented toward long‑term value creation.

We’re seeking an AI/ML Full Stack Engineer to join the elite team scaling the technology behind our Predictive Intelligence platform. You’ll work at the intersection of machine learning, data engineering, and product development—creating the tools that allow clients to see weeks or months into the future with unprecedented accuracy. This is an excellent opportunity for an engineer looking to grow their AI/ML and full‑stack skills in a high‑impact environment.

You will work across the full stack, from data ingestion and modeling to APIs and client‑facing interfaces. This role goes beyond basic analytics or dashboarding. You will help design and product ionize systems that integrate large‑scale, real‑time, multi‑source data, apply predictive and causal modeling, and continuously learn from new signals.

This is an ideal role for an engineer who wants deep exposure to real‑world AI systems, complex data environments, and mathematically grounded prediction at scale.

• Design, build, and maintain full‑stack applications that deliver predictive insights through scalable, user‑facing interfaces.
• Develop and maintain automated data pipelines for ingesting, processing, and transforming large‑scale structured and unstructured data.
• Implement, deploy, and maintain machine learning models including forecasting, regression, classification, and ensemble methods.
• Apply statistical and machine learning techniques to support forward‑looking analysis and decision‑making.
• Build and maintain backend services, APIs, and integrations that support analytics and modeling workflows.
• Support experimentation, testing, and validation of models and analytical outputs.
• Work with modern AI approaches, including large language models, to support automation and data‑driven workflows.
• Optimize system performance, reliability, and scalability across data pipelines and ML services.
• Collaborate cross‑functionally with engineering, product, and analytics teams to translate requirements into technical solutions.
• Contribute to best practices for code quality, monitoring, and operational stability of production ML systems.

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 2–4 years of professional experience in software engineering with exposure to full‑stack development.
• Strong proficiency in Python for data processing, modeling, and backend services.
• Experience with JavaScript/Type Script and modern frontend frameworks such as React or similar.
• Hands‑on experience building, training, evaluating, or deploying machine learning models in production or near‑production environments.
• Solid grounding in statistics, probability, linear algebra, and predictive modeling concepts.
• Experience working with time‑series data, forecasting, regression, or classification problems.
• Strong SQL skills and experience working with relational and/or analytical databases.
• Experience building and consuming REST or Graph
  
  QL APIs.
• Familiarity with cloud platforms such as AWS, GCP, or Azure and cloud‑native architectures.
• Ability to reason about data quality, bias, model assumptions, and uncertainty.
• Strong problem‑solving ability and comfort working in ambiguous, evolving technical…