Machine Learning Engineer

Overview

THE POSITION
Our roster has an opening with your name on it

We’re looking for a Machine Learning Engineer to join our growing team and help design, build, and deploy machine learning systems that power real-world applications. In this role, you’ll work closely with data scientists, engineers, and product managers to bring models from experimentation to production and ensure they perform reliably at scale.

You’ll contribute across the ML lifecycle—including feature engineering, model training, evaluation, deployment, and monitoring—while growing your skills in software development, ML Ops, and scalable infrastructure.

If you’re excited by this challenge and want to work within a dynamic company, then we’d love to hear from you.

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
:
Everyone on our team has a part to play

• ML Pipeline Development
• Collaborate with data scientists to implement and optimize machine learning models for production use.
• Develop and maintain pipelines for data preparation, training, and model deployment.
• Build tools and services to support real-time and batch inference workloads.

• Collaboration & Execution

• Translate product and business requirements into ML-driven solutions.
• Participate in agile workflows, including sprint planning, code reviews, and design discussions.
• Work with engineers and analysts to ensure data integrity and efficient feature computation.

• Quality & Reliability

• Implement monitoring and alerting to track model performance and detect issues such as data drift.
• Write maintainable, testable code and follow best practices in version control and documentation.
• Help automate training, deployment, and retraining workflows using ML Ops tools.

THE ST
ACK
:
Databricks, AWS, Spark, Python, MLFlow , (Generally available ML Libraries) , Terraform, Github , Buildkite

What we're looking for in our next teammate

• 2–4 years of experience in software engineering, machine learning, or data science.
• Proficiency in Python, with exposure to ML libraries (Scikit-learn, Tensor Flow, or PyTorch).
• Solid understanding of data structures, algorithms, and software engineering principles.
• Hands-on with SQL and comfortable working with large datasets.
• Familiarity with distributed computing (Apache Spark preferred).
• Exposure to ML deployment & monitoring practices or strong interest in learning them.
• Bonus: experience with Databricks, MLflow, or similar ML Ops tools.
• Experience with cloud services (AWS preferred, GCP or Azure also valuable).

Preferred Qualifications

• Experience with containerization (Docker, Kubernetes is a plus).
• Familiarity with orchestration/ML Ops tooling (Sage Maker, MLflow).
• Understanding of model evaluation metrics and techniques for improving generalization.
• Interest in or experience with real-time ML systems, recommendation engines, or NLP.

About You

You might be a great fit if you often ask yourself questions like:

• “How do complex systems actually work end to end, and how can I make them better?”
• “What makes software reliable, and how do you design for that from the start?”
• “Where’s the balance between moving fast and building things that last?”
• “How do small changes in code or data ripple out into big user or business impacts?”
• “What can I automate today that will save everyone headaches tomorrow?”
• “How do different roles ( engineers, data scientists, product managers, etc.) fit together to ship something meaningful?”
• “What skills should I grow next if I want to level up from strong engineer to strong ML engineer?”

This role will join our Personalization team, working directly with senior engineers to:

• Build and optimize ML pipelines and feedback loops for our flagship recommender systems.
• Improve observability, monitoring, and on-call reliability across models.
• Partner with Fin Ops to optimize Spark jobs and cloud resource usage.
• Adopt and integrate AI Foundations platform tools into workflows.

This person will have mentorship from Staff ICs and the opportunity to grow,…