Staff ML Engineer

About the Product

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 build, you code, you ship, 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

First Year Plan

During the 1st 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 robustmonitoring and

feedback loops

Collaborate with product and engineering to define success metrics andintegration

strategies

Establish early ML development workflows, documentation, andperformance monitoring

Contribute production-ready code for feature engineering and modelexperimentation

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 grow the team through hiring, mentorship, and a culture of innovation

What We Are Looking For

Experience : 8+ years of professional experience in software development or data science, with at least 5+ years specifically focused on building and deploying machine learning models in a production environment

Proven Impact : Demonstrable track record of successfully shipping multiple machine learning models that resulted in measurable business growth (e.g., increased user engagement, conversion rates, operational efficiency, revenue). You can clearly articulate the business problem, the ML solution, and the quantitative impact achieved

Technical Depth (Hands-On) :

Expertise in Python and common ML libraries / frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Keras, XGBoost)

Deep understanding of core ML concepts (e.g., classification, regression, clustering, recommendation systems, NLP, time series analysis, experimentation, model evaluation)

Strong SQL skills and experience working with large datasets and data processing tools (e.g., Pandas, Spark)

Experience with ML deployment patterns and MLOps principles (e.g., model serving, monitoring, CI / CD for ML, feature stores)

Familiarity with cloud platforms (AWS, GCP, Azure) is essential

Business Acumen : Strong ability to understand business needs, translate them into well-defined ML problems, and connect technical work back to strategic objectives. You prioritize work based on potential business impact

Leadership Experience : Experience or a strong aptitude for leading technical projects, defining technical direction, and mentoring others. Excellent communication and collaboration skills

Education : MS or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field, OR equivalent practical experience demonstrating deep expertise in machine learning

Bonus Points

Experience building ML capabilities from the ground up

Experience with recommendation systems, search ranking algorithms, or NLP applied to user-generated content

Experience in the EdTech or consumer-facing platform space

Familiarity with golang, express, Postgres, Snowflake, DBT, and Tableau

Contributions to open-source ML projects or publications in relevant conferences / journals

We offer

We welcome new ideas and allow you to make an immediate impact on the team.

Flexible Paid time off (PTO for any reason, including sick days (no specified limits) and flexible work schedule.

Personal laptop.

Health / Sport Budget.

Fully remote.