Staff Machine Learning Engineer

About Niche

Niche is the leader in school search. Our mission is to make researching and enrolling in schools easy, transparent, and free. With in-depth profiles on every school and college in America, 140 million reviews and ratings, and powerful search tools, we help millions of people find the right school for them. We also help thousands of schools recruit more best-fit students, by highlighting what makes them great and making it easier to visit and apply.

Niche is all about finding where you belong, and that mission inspires how we operate every day. We want Niche to be a place where people truly enjoy working and can thrive professionally.

 

About The Role

We are looking for our first Staff Machine Learning Engineer to establish and lead our machine learning initiatives. This is a critical, foundational role where you will have the unique opportunity to shape the future of data science and ML at Niche. You will be responsible for identifying high-impact opportunities, designing, building, and deploying machine learning models that directly drive business growth and enhance user experience across our platform.

We are looking for a highly skilled, hands-on practitioner who is passionate about translating business challenges into data-driven solutions. You aren't just theoretical; you build, you code, you ship, and you measure. You have a proven history of deploying ML models into production environments that have delivered tangible results.  You possess the experience and desire to mentor future ML hires and establish best practices as our capabilities grow.

 

What You Will Do

• Identify & Prioritize: Collaborate closely with product, engineering, data analytics, and business stakeholders to identify and prioritize the most impactful ML opportunities that align with Niche's strategic goals. Our first area of focus is our Recommendations, which includes matching students with the right schools
• Design & Build: Lead the end-to-end development of machine learning models – from data collection and feature engineering to algorithm selection, training, tuning, and validation. This is a hands-on coding role
• Deploy & Integrate: Develop production-grade code and systems to deploy, serve, and monitor ML models at scale, ensuring reliability and performance. Integrate models effectively into Niche’s products and internal systems
• Measure & Iterate: Define key performance metrics, establish robust monitoring frameworks, analyze model performance in production, and drive continuous improvement through iteration and experimentation
• Champion & Evangelize: Clearly communicate complex ML concepts, model behaviors, and results to both technical and non-technical audiences. Champion the use of machine learning & data science across the organization
• Lead & Mentor: Establish ML development best practices, coding standards, and documentation. As the function grows you will guide and mentor other ML engineers
• Innovate: Stay abreast of the latest advancements in machine learning, data science, and MLOps, evaluating and potentially adopting new technologies and techniques relevant to Niche

During the First Month:

• Learn about Niche by meeting with various team members to learn more about our company through our Onboarding meetings
• Deep-dive into Niche’s platform, data architecture, and recommendation systems
• Align with product and engineering teams on business goals and ML impact areas
• Begin shaping a roadmap for high-impact ML opportunities

Within 3 Months:

• Deploy your first machine learning model into production with robust monitoring and feedback loops
• Collaborate with product and engineering to define success metrics and integration strategies
• Establish early ML development workflows, documentation, and performance monitoring
• Contribute production-ready code for feature engineering and model experimentation

Within 6 Months:

• Drive measurable improvements through experimentation and model iteration
• Introduce scalable MLOps practices to support deployment, retraining, and governance
• Serve as a mentor and set engineering best practices for a growing ML function

Within 12 Months:

• Lead ML efforts across multiple product areas, driving business impact at scale
• Influence company-wide strategy through technical leadership and ML evangelism
• Develop internal tooling, reusable frameworks, and scalable ML systems
• Help