Hybrid: NYC, West side Chelsea, Hudson yards.
They'll have competitive compensation packages to big tech.
stock options, given every year that vest every 4 years.
Are you a Machine Learning Leader who thrives at the intersection of cutting-edge applied research, complex system architecture, and team mentorship?
Do you want to orchestrate the algorithms and infrastructure that process massive, real-time data streams to drive immediate, multi-million dollar business impact at a global scale?
We are looking for ambitious, applied researchers and engineers who want to rival the tech stacks of Google and Meta, helping us become the biggest mobile programmatic advertising platform in the world.
The Challenge Ad-tech is one of the most demanding environments for machine learning. You will be operating at massive scale under strict latency constraints, solving unique modeling challenges that aren't even covered in current scientific literature. Forget off-the-shelf APIs and standard tutorials.
You will be architecting, training, and deploying state-of-the-art deep learning models that process millions of requests per second. Whether you are adapting the latest transformer architectures or fine-tuning massive neural networks for sub-millisecond inference, you will be pushing the absolute limits of predictive modeling. This is where cutting-edge academic research meets ruthless, real-time execution.
You will directly tackle complex applied research problems like extreme positive sample sparsity and complex labeling delays, turning theoretical concepts into high-performance, production-ready code that immediately impacts the bottom line.
The Focus Setting the technical vision, driving major architectural decisions, and mentoring the team to build systems that scale with our massive revenue growth.
What You Will Do:
- Architect the Machine Learning Engine: Dictate the technical direction of our core algorithms and big data infrastructure. You will design fault-tolerant, low-latency systems that can effortlessly scale alongside our massive global growth.
- Define the Applied Research Frontier: Cut through the industry hype to identify which emerging technologies—like novel transformer architectures or advanced LLMs—will actually drive explosive business value. You will map out our ML research and development roadmap.
- Multiply Engineering Impact: You are a force multiplier. Mentor, elevate, and lead a high-performing squad of ML engineers and data scientists, fostering a ruthless culture of rigorous engineering, rapid experimentation, and applied research excellence.
- Bridge Deep Tech and Business Strategy: Act as the linchpin between the engineering org and the executive team. You will translate complex deep learning performance metrics directly into strategic business wins, tying your team's output to our revenue goals.
- Solve the Hardest Problems: You aren't just managing Jira tickets; you are the ultimate technical escalation point. You will still roll up your sleeves, write code, and tackle the most mathematically and computationally intensive modeling challenges we face.
- Experience: 5+ years of applied ML experience, with a proven track record in a technical leadership or architectural role.
- System Design Mastery: Expert-level knowledge of designing, deploying, and maintaining high-throughput, low-latency machine learning architectures in production at a massive scale.
- Deep Tech Stack Fluency: Mastery of Big Data ecosystems (AWS, Apache Spark) and fluency in transitioning models from research to high-performance C++/Java/Scala/Python production environments.
- Business Acumen: Ability to tie complex deep learning metrics directly to business KPIs (e.g., ad spend efficiency, revenue growth).
- Communication: Exceptional verbal and written communication skills to articulate technical trade-offs to non-technical stakeholders.
- Deep Academic Roots: PhD in Machine Learning, AI, or a related field, potentially with published research in relevant domains.
- Enterprise Architecture: Experience designing, scaling, and migrating massive big data architectures across AWS (EMR, Athena, Redshift) and Apache Spark at an enterprise level.
- AI Strategy: Experience spearheading the integration of advanced LLMs and transformer architectures into legacy systems to drive new revenue streams.
- Deep Industry Expertise: Subject matter expert in programmatic advertising, Real-Time Bidding (RTB), Demand Side Platforms (DSPs), and global user acquisition strategies.
- Thought Leadership: A history of mentoring senior engineers, speaking at tech conferences, or contributing to open-source ML projects.
