Machine Learning Engineer
Posted on 9/20/2023

51-200 employees

Automated platform for modern, scalable TV advertising
Company Overview
Madhive is a leading force in modern TV advertising technology, offering a self-service platform that streamlines the ad buying process, providing advertisers with enhanced simplicity, accountability, and control. The company's robust infrastructure, which processes 260 billion ad opportunities daily, ensures precise, brand-safe audience targeting at scale, and is trusted by major content owners, creators, and distributors, including FOX and TEGNA's Premion. With a customizable, full-stack platform that can integrate with existing data and systems, Madhive is uniquely positioned to assist partners in capitalizing on the shift from linear to digital advertising.
Data & Analytics

Company Stage


Total Funding





New York, New York

Growth & Insights

6 month growth


1 year growth


2 year growth

Remote in USA
Experience Level
Desired Skills
Data Science
Data Structures & Algorithms
Development Operations (DevOps)
Google Cloud Platform
AI & Machine Learning
  • Minimum of 5+ years of experience in maintaining scalable, resilient backend systems, with around 3 years experience developing production-level deep learning models
  • "T"-shaped engineer with competency across a broad range of technologies and deep expertise in at least one technical domain
  • Experience of core ML technologies such as TensorFlow, BigQuery, Bigtable, Dataflow, Kubernetes, and TFX Serving with the ability to utilize these in our backend systems
  • Proficiency in data manipulation, feature engineering, and model development
  • Proficiency in deep learning frameworks
  • Strong ownership mentality and a passion for staying at the cutting edge of technology
  • Ability to autonomously manage complex scopes of work and deliver product-grade code with minimal guidance
  • Collaborative mindset, ability to synthesize and distill information across diverse perspectives, and effective cross-departmental collaboration skills
  • Background diversity is encouraged; previous experience in adtech is not mandatory
  • Develop machine learning models that predict, classify, and generate data to enhance various facets of our platform, including sophisticated bidding strategies, search functionalities, and user experience enhancements
  • Oversee MLOps processes, ensuring visibility into model performance, facilitating Canary and Red/Green model deployments, and managing retraining and fine-tuning efforts
  • Develop and implement machine learning models and algorithms that address specific business problems and requirements
  • Collect, preprocess, and clean large datasets to ensure data quality and usability for training machine learning models
  • Create and engineer relevant features to improve model performance and accuracy
  • Train, fine-tune, and optimize machine learning models using various techniques, including deep learning, reinforcement learning, and ensemble methods
  • Evaluate the performance of machine learning models using appropriate metrics and conduct A/B testing to validate their effectiveness
  • Collaborate with DevOps and software engineering teams to deploy machine learning models into production environments
  • Monitor model performance in production, implement model updates, and address any issues that arise
  • Stay up-to-date with the latest advancements in machine learning and artificial intelligence and apply them to solve real-world problems
  • Maintain comprehensive documentation of models, datasets, and code to ensure knowledge sharing and reproducibility
  • Work closely with data scientists, software engineers, and product managers to align machine learning efforts with business goals and requirements
  • Leverage Google Cloud Platform (GCP) as our exclusive infrastructure provider, while also collaborating with a language-agnostic engineering team
  • Participate in an on-call rotation schedule to provide timely response and support for engineering-related issues outside of regular business hours, ensuring the continuous operation of critical systems and infrastructure
Desired Qualifications
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field. Ph.D. is a plus