We are seeking a Mid-Level Machine Learning Engineer to join our growing Data Science & Engineering team.
In this role, you will design, develop, and deploy ML models that power our cutting-edge technologies like voice ordering, prediction algorithms, and customer-facing analytics.
You will collaborate closely with data engineers, backend engineers, and product managers to take models from prototyping through to production, continuously improving accuracy, scalability, and maintainability.
Essential job functions include model development, feature engineering, experimentation and evaluation, owning the entire modeling lifecycle, monitoring and maintenance, collaboration and mentorship, and documentation and communication.
Requirements:
Candidates must have a Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field.
A minimum of 5 years of industry experience (or 1+ year post-PhD) is required.
Proven experience in building and deploying advanced machine learning models that drive business impact is essential.
Candidates must have hands-on experience shipping production-grade ML models and optimization systems, including expertise in experimentation and evaluation techniques.
Proficiency in Python and libraries such as pandas, NumPy, and scikit-learn is required, along with familiarity with TensorFlow or PyTorch.
Hands-on experience with at least one cloud ML platform (AWS SageMaker, Google Vertex AI, or Azure ML) is necessary.
Candidates should have hands-on experience with SQL and NoSQL databases and be comfortable working with Spark or similar distributed frameworks.
A strong foundation in statistics, probability, and ML algorithms like XGBoost/LightGBM is required, along with the ability to interpret model outputs and optimize for business metrics.
Experience with categorical encoding strategies and feature selection is necessary.
A solid understanding of regression metrics (MAE, RMSE, R²) and hyperparameter tuning is required.
Proven skills in deploying ML solutions in AWS, GCP, or Azure, along with knowledge of Docker, Kubernetes, and CI/CD pipelines, are essential.
Excellent communication skills and the ability to translate complex technical concepts into clear, actionable insights are required.
Candidates must be flexible and work during US hours at least until 6 p.m. ET in the USA and must have their own system/work setup for remote work.
Benefits:
The position offers the opportunity to work on cutting-edge technologies and collaborate with a talented team.
You will have the chance to mentor junior engineers and contribute to the advancement of the company's capabilities and applications.
The role allows for remote work, providing flexibility in your work environment.
You will gain experience with advanced tools and platforms in the machine learning field, enhancing your professional development.
