Madhive is seeking a Machine Learning Engineer to assume a pivotal role in the development and deployment of cutting-edge machine learning models and algorithms that drive our products and services. In this role, you will collaborate closely with cross-functional teams to design, construct, and implement machine learning solutions that directly impact our business success. 

At Madhive, we operate on a massive scale, handling millions of requests per second and processing petabytes of data daily. As a Machine Learning Engineer, you’ll be at the forefront of technology, working on diverse projects that align with your interests, abilities, and development goals.

What you’ll do:

• 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.

Who you are:

• Minimum of 5+ years of experience in maintaining scalable, resilient backend systems, with around 3 years experience developing production-level deep learning models.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field. Ph.D. is a plus.
• "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.

The approximate compensation range for this role is $200,000-$250,000. The actual offer, reflecting the total compensation package and benefits, will be determined by several factors, including the applicant’s experience, knowledge, skills, and abilities, as well as internal equity among our team.