Machine Learning/LLM Foundation Lead

About the Role

We are building one of the most advanced machine learning platforms in mobile programmatic advertising. Our goal is simple but ambitious: build a tech stack capable of competing with the most sophisticated advertising and AI systems in the world, including those at companies like and .

We are looking for an experienced Principal Machine Learning Engineer / ML Architect who thrives in high-scale environments and wants to work on deeply technical, real-world machine learning challenges. This role sits at the intersection of applied research, distributed systems, and large-scale machine learning infrastructure .

You will architect and deploy cutting-edge deep learning models that operate under strict latency constraints while processing millions of requests per second . This is a role for someone who enjoys solving complex problems and turning advanced ML research into production systems that directly drive revenue.

What You’ll Do

• Architect large-scale ML systems: Design and build high-throughput, low-latency machine learning systems that power our advertising platform.

• Develop advanced models: Build and optimize deep learning models for prediction, ranking, and recommendation at massive scale.

• Define the ML roadmap: Evaluate emerging technologies such as transformer architectures and large-scale neural networks to determine which innovations will drive real business impact.

• Build scalable data infrastructure: Work with distributed systems and big data frameworks to support large-scale training and inference pipelines.

• Lead and mentor: Guide a team of ML engineers and data scientists, fostering a culture of strong engineering practices, experimentation, and technical excellence.

• Bridge engineering and business: Translate model performance improvements into measurable business impact such as revenue growth and ad performance.

• Solve complex problems: Tackle difficult modeling challenges including delayed feedback loops, sparse signals, and real-time inference constraints.

What You Bring

• 5+ years of experience building and deploying machine learning systems in production environments.

• Strong expertise in deep learning, predictive modeling, and large-scale ML systems .

• Experience designing high-throughput, low-latency architectures for real-time machine learning applications.

• Proficiency in programming languages such as Python, Java, Scala, or C++ .

• Experience working with distributed data and compute frameworks such as Apache Spark and large-scale cloud environments like AWS