Machine Learning Director
At a glance
Director-level Machine Learning Engineering role at PayJoy. San Francisco · full-time · $300,000–$321,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Set the long-range direction for ML platforms, model delivery, internal APIs, and the data-product pipeline
- Lead and develop Staff and Senior ML Engineers, while raising the bar for system design, scale, and code quality
- Connect data science and engineering with Risk, Fraud, and Product teams, turning business goals into scalable technical work
- Guide deployment, monitoring, and MLOps, keeping data products reliable, maintainable, and aligned with global safety standards
- Evaluate newer approaches such as LLMs and graph databases without compromising platform stability
What you bring
- A graduate degree in computer science, statistics, engineering, or a related discipline
- At least eight years in data science or ML engineering, including four years leading senior or staff-level engineers
- A record of building and delivering large-scale machine learning systems
- Command of the ML lifecycle, from feature extraction through production monitoring, and the judgment to review architecture
- Strong communication skills for explaining technical choices to nontechnical partners and executive leaders
Who this fits
This director role suits a hands-on ML platform leader who thinks beyond individual models and can guide senior engineers. It is based in San Francisco with a hybrid work arrangement.
From the employer
As PayJoy’s ML Platform Director, you will lead a team of very talented Machine Learning Engineers and Software Engineers that are responsible for the end-to-end ML ecosystem at PayJoy. This includes ML infrastructure: feature stores (online and offline), model serving, ETL/feature architecture and the infrastructure that helps us serve our customers like our internal offers API.
You will bridge the gap between business strategy (Risk, Fraud, Product) and technical execution, ensuring that our ML infrastructure is scalable, secure and world-class. You are a leader who enables senior-level engineers to solve the company’s most complex problems, fostering an environment where innovation meets production-grade reliability.
You will be part of a data science team on a mission to improve access to credit and technology in emerging markets with the opportunity of creating a big and real positive impact to our millions of users across the countries we operate in.
Key Responsibilities
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Define the technical roadmap for our ML platform, modeling and internal APIs infrastructure, moving beyond individual projects to oversee the long-term sustainability of our "assembly line" approach to data products and ML model + offers serving.
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Mentor and manage a team of Staff and Senior-level ML Engineers. Foster a culture of technical excellence, focusing on system design, scalability and code quality.
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Act as the primary liaison between Data Science/ML Engineering and business stakeholders like Risk, Fraud, Engineering and Product. Translate complex business goals into actionable technical requirements that the team can execute at scale.
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Own the long-term vision for our ML infrastructure, including deployment, monitoring and MLOps practices. Ensure that all data products are not only performant but also maintainable and compliant with global safety standards.
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Guide the professional growth of your direct reports, ensuring they are challenged by the right problems and supported by clear career trajectories.
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Champion the adoption of new technologies (e.g., LLMs, graph databases) and best practices that keep PayJoy at the forefront of financial ML without sacrificing stability.
Requirements
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PhD or master’s in Computer Science, Statistics, Engineering or a related field
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8+ years of hands-on experience in Data Science or ML Engineering, with at least 4+ years in a leadership role managing senior-level or staff-level engineers.
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A proven track record of designing and delivering large-scale ML systems. You must have a deep understanding of the full ML lifecycle (from feature extraction to production monitoring) and the ability to review system designs and architecture at a high level.
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Ability to identify bottlenecks in global ML operations and design systematic solutions. You think in terms of platforms, not just individual models.
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Exceptional ability to communicate complex technical concepts to non-technical stakeholders. You can defend technical decisions to executive leadership and advocate for the "engineering mindset."