Machine Learning/LLM Foundation Lead

Principal Machine Learning Engineer / ML Architect (AdTech)
Location: NYC 10001 - Hybrid 
Experience: 5+ years in Applied Machine Learning
Direct Hire W2

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