The ideal candidate is local to and interested in working from any of our offices (Silver Spring, NYC, SF, Miami, Denver) local to them 1-2x per week.ABOUT THE ROLE As a Staff Machine Learning Engineer at Rocket Money, you will be at the forefront of our ML and AI product development, bringing your expertise to design, implement, and maintain sophisticated ML systems that drive our product experiences. You will:
- Lead the architecture and development of complex AI and ML powered features across Rocket Money's product suite, proactively identifying technical challenges before they become issues.
- Design, implement, and maintain robust evaluation frameworks. You ensure that ML/AI systems can be constantly improved and create frameworks so that others can do the same.
- Develop novel new product experiences that you are uniquely positioned to enable for users. Help Rocket Money capitalize on it's unique dataset and scale. Guide others to help generate impact through effective technical leadership and partnership with product.
- Own end to end development and implementation of ML and AI product features in collaboration with cross-functional product development teams. Rigorous technical critique and oversight coupled with effective communication of business impact are musts.
- Provide technical mentorship, fostering an environment where everyone can contribute to high-impact work. You will raise the level of everyone around you.
Potential Projects:
- Develop comprehensive evaluation and ML Ops frameworks that enable systematic assessment of model performance, supporting rapid iteration and improvement of AI/ML product features.
- Scale transaction metadata generation systems to billions of financial transactions in a consumer facing app. Implement AI assisted transaction management and personalization features such as personalized categories, income and rent detection, and subscription identification.
- Implement effective user feedback mechanisms that enable measurement of ML feature product quality and constant improvement.
- Architect user facing predictive features that depend on fast moving and large scale user transaction data.
ABOUT YOU - 8+ years of professional experience in machine learning engineering or data science roles, with a proven track record of designing and implementing ML systems at consumer tech scale and speed.
- Extensive hands-on experience integrating ML and AI methods into production workflows, including creating evaluation tooling and effective user feedback mechanisms to support rapid product development cycles and systematic model evaluation.
- Experience with prompt engineering and management, creating robust systems for testing and optimizing LLM-based applications.
- Expert-level proficiency in Python, SQL, and at least a handful of common ML frameworks. Working Typescript knowledge is a plus. You understand ML methods at a fundamental level - deep experience in NLP techniques is a plus.
- You are a master at taking ambiguous problems, creating clarity, and breaking down work into manageable chunks for implementation. You've owned the development, launch, and maintenance for several scaled ML/AI powered product experiences.
- You understand basic software engineering and computer science fundamentals and have applied them at consumer grade scale to build ML powered products in production environments.
- You are a technical leader who can identify both emergent technical opportunities and gaps relative to best practice. Your strong communication skills enable your team to capitalize on these opportunities and generate impact.
- You care about products and user experiences, not publications. You are a practitioner first and thrive at the (messy) intersection of the real world and cutting edge approaches.
WE OFFER - Health, Dental & Vision Plans
- Competitive Pay
- 401k Matching
- Unlimited PTO
- Lunch daily (in-office only)
- Snacks & Coffee (in-office only)
- Commuter benefits (in-office only)
Additional information: Salary range of $210,000 - $260,000/year + bonus + benefits. Base pay offered may vary depending on job-related knowledge, skills, and experience.
