Staff Machine Learning Engineer

POSITION: STAFF MACHINE LEARNING ENGINEER

We are excited to share a VIP opportunity for our first Staff Machine Learning Engineer to build and lead machine learning efforts across the organization. This is a foundational role offering the chance to define the ML roadmap, set technical standards, and directly influence the future of our data science function.

The role is highly hands-on and impact-driven—from identifying high-value use cases to building, deploying, and scaling production ML models that improve product performance and business outcomes. Beyond technical ownership, this position offers the opportunity to mentor future hires and shape best practices as the ML team grows.

This role comes with the visibility, autonomy, and growth potential expected from a VIP position, along with competitive benefits and the ability to make a lasting impact from day one.

Key Responsibilities

Identify & Prioritize: Partner with product, engineering, analytics, and business teams to drive high-impact ML initiatives, with a focus on student–school recommendation systems.

Build End-to-End: Own the full ML lifecycle—from data and features to model design, training, and validation—in a hands-on engineering role.

Deploy & Scale: Develop production-grade code and scalable systems to integrate ML models into products and internal tool

Measure & Improve: Define success metrics, monitor production performance, and continuously optimize through experimentation.

Communicate & Lead: Explain ML insights to diverse stakeholders, establish best practices, and mentor junior engineers as the team grows.

Innovate: Stay current with ML and MLOps advancements and evaluate new tools for adoption.

Requirements

Experience:
8+ years of experience in software development or data science, including 5+ years of hands-on ML development and deployment in production.

Proven Impact:
Strong track record of delivering ML solutions that produced measurable business improvements (e.g., higher engagement, conversions, operational efficiency, or revenue). You can clearly explain the business problem, your ML approach, and the resulting impact.

Technical Expertise:

Advanced Python skills with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost, Keras)

Deep knowledge of ML fundamentals (classification, regression, clustering, recommendations, NLP, time series, experimentation)

Strong SQL and experience working with large datasets and tools like Pandas or Spark

Practical MLOps experience (model serving, monitoring, CI/CD for ML, feature stores)

Familiarity with cloud platforms (AWS, GCP, or Azure)

Business Acumen:
Ability to translate business needs into ML solutions and prioritize based on expected impact.

Leadership:
Experience leading technical initiatives, mentoring engineers, and communicating effectively across functions.

Education:
MS/PhD in a quantitative field (CS, Statistics, Math) or equivalent practical expertise in ML.

Bonus Skills

• Experience building ML capabilities from scratch

• Background in recommender systems, search ranking, or NLP

• Experience in EdTech or large consumer platforms

• Familiarity with Golang, Express, Postgres, Snowflake, DBT, or Tableau

• Open-source ML contributions or publications