Senior ML Engineer, Product

Rocket Money•San Francisco, WA

23h

About The Position

Develop and maintain reusable ML pipelines and systems, ensuring models are well-integrated with other systems via comprehensive testing and documentation. Collaborate closely with cross-functional teams to provide critical input on technical direction. You will use your ML skills to enhance user experiences and meet business needs by collaborating on strategy in addition to technical implementation. Strong focus on model monitoring and optimization, building systems for performance tracking, drift detection, alerting, and resource optimization. Set up deployment infrastructure including setting up APIs and implementing automated monitoring and deployment processes. Be a steward of good instrumentation and experimental design — design systems to measure the impact of ML powered products in a way that is measurable, testable, repeatable, and robust. Establish evaluation criteria for ML use cases, including but not limited to fine-tuned LLMs. Build and manage data labeling and data ingestion frameworks, optimizing workflows to improve the agility of data pipelines and data scientists' experiences. Become an expert on our members. Understand their needs and financial goals. Work with product to define strategy and engineering teams to create software and build features that help our members build better financial lives. Maintain a high technical bar by mentoring junior team members, participating in code reviews, and ensuring quality in production systems. Potential Projects Create, diagnose, and evaluate LLM agents that successfully cancel and negotiate cancellations for our users. Uncover and exploit relationships between customers’ subscriptions, purchase, and transaction data as you build personalized product experiences and power ever more accurate customer segmentation, propensity, and affiliate targeting models. Build anomaly detection systems, ensuring that our transaction categorization systems produce accurate data for our users and tracking when they don’t.

Requirements

5+ years of professional experience working in a data science or machine learning engineering capacity.
Proficient in SQL, Python and have strong software engineering skills regardless of specific language.
Contribute up and down the application stack to deliver data products to users.
Evidenced experience working within engineering teams to build software is an absolute must.
Team player — collaboration and communication are a first instinct and key tool for getting stuff done.
Continually seek feedback on your work and err on the side of over-communicating.
Capable of influencing technical direction while also guiding teams through challenges.
Enthusiastic and avidly research the cutting edge solutions in the world of ML — experience with tools such as RAG and LLM evaluation techniques are essential.
Continue to grow and learn and are excited by hard problems and big challenges.
Care just as much about why you're solving a problem as the solution.
Always want a deep understanding of context and business impact.
ML engineer first but an expert data scientist and analyst when necessary.
Excellent writing, presentation, and communication skills.
Equally adept at hacking together proof of concepts and working within engineering teams to build scalable, durable systems.
Know how to deliver products incrementally — doing the simple thing first, finding and measuring signal, and iterating to build better user experiences.
Deep experience in several of the following in a professional capacity: building generative AI applications, computer vision, deep learning architectures, anomaly detection, reinforcement learning, feature engineering at scale, MLOps and model deployment, distributed computing with big data, or system design and architecture.

Nice To Haves

Experience in fintech, banking, or finance is a plus.

Responsibilities

Develop and maintain reusable ML pipelines and systems, ensuring models are well-integrated with other systems via comprehensive testing and documentation.
Collaborate closely with cross-functional teams to provide critical input on technical direction.
Enhance user experiences and meet business needs by collaborating on strategy in addition to technical implementation.
Focus on model monitoring and optimization, building systems for performance tracking, drift detection, alerting, and resource optimization.
Set up deployment infrastructure including setting up APIs and implementing automated monitoring and deployment processes.
Design systems to measure the impact of ML powered products in a way that is measurable, testable, repeatable, and robust.
Establish evaluation criteria for ML use cases, including but not limited to fine-tuned LLMs.
Build and manage data labeling and data ingestion frameworks, optimizing workflows to improve the agility of data pipelines and data scientists' experiences.
Understand members' needs and financial goals.
Work with product to define strategy and engineering teams to create software and build features that help members build better financial lives.
Maintain a high technical bar by mentoring junior team members, participating in code reviews, and ensuring quality in production systems.