Machine Learning Engineer

Breeze Airways™•Cottonwood Heights, UT

About The Position

Working at Breeze Airways is an exciting endeavor and a serious commitment to bring “The World’s Nicest Airline” to life. We work cross-functionally with truly awesome Team Members to deliver on our mission: “To make the world of travel simple, affordable, and convenient. Improving our guests travel experience using technology, ingenuity and kindness.” Breeze is hiring- join us! As a Machine Learning Engineer at Breeze Airways, you will lead the development of systems, tools, and processes that enable our Data Science/ML team to turn models into reliable, scalable production solutions. You will own the end-to-end machine learning lifecycle—from feature engineering and model training to deployment, monitoring, and continuous improvement in production. You will design and evolve the architecture and workflows that support how models move from experimentation to real-world impact, partnering closely with Data Science, Product, DevOps, and other engineers to establish consistent, automated, and repeatable delivery patterns. Your work will directly support revenue optimization, operational performance, and guest experience initiatives. You thrive in a growing, fast-moving environment and are comfortable building practical, production-ready systems from the ground up. You bring strong engineering fundamentals, sound judgment around tooling and architecture, and a collaborative mindset to deliver durable, high-impact machine learning products.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
3+ years of professional experience in machine learning engineering, data science, or software engineering with a focus on production ML systems.
Demonstrated experience deploying, operating, and maintaining ML models in production environments (batch and real-time).
Strong proficiency in Python and experience with modern ML libraries and frameworks (e.g., scikit-learn, XGBoost, LightGBM, PyTorch).
Experience working with cloud-based data platforms such as Snowflake.
Working knowledge of containerization, API development, and system integrations (e.g., Docker, FastAPI, REST services).
Experience collaborating closely with data scientists, data engineers, and platform teams to deliver production ML solutions.
Ability to design, document, and support reliable ML pipelines and workflows.
Self-directed, adaptable, and comfortable operating in ambiguous, fast-growing environments.

Nice To Haves

Experience deploying and operating ML systems on cloud platforms such as AWS SageMaker (or equivalent).
Hands-on experience with model registries, experiment tracking, and reproducibility tools (e.g., MLflow, SageMaker Model Registry).
Experience implementing model monitoring and governance practices, including data drift, performance degradation, and retraining workflows.
Exposure to airline, travel, e-commerce, or marketplace environments—especially in pricing, personalization, forecasting, or demand modeling.
Experience building and maintaining production APIs for ML services (e.g., FastAPI, Flask).
Proven ability to integrate ML models into core business workflows and operational systems (beyond standalone model serving).
Working knowledge of CI/CD pipelines for ML and data products (e.g., GitHub Actions, GitLab CI, Jenkins).
Familiarity with infrastructure-as-code and environment management practices (e.g., Terraform, CloudFormation, Docker, Conda).
Experience deploying, evaluating, or operating LLM-based systems in production environments.
Track record of owning ML projects end-to-end, from data ingestion and modeling through deployment and monitoring.
Experience thriving in small, fast-moving teams with high autonomy and accountability.
Comfort building foundational ML platforms, standards, and tooling from the ground up, rather than inheriting mature systems.
Strong problem solving skills with the ability to debug complex systems spanning data, code, and infrastructure.
Production mindset—understands the difference between code that works and code that's ready for production, and takes ownership of system reliability.
Ability to balance building robust, scalable solutions with pragmatic delivery timelines. With a small Data Science team, you'll focus on high-leverage solutions over elaborate infrastructure.
Excellent collaboration skills—comfortable working with data scientists who focus on modeling and engineers who focus on infrastructure.
Clear written and verbal communication to document systems and explain technical decisions to varied audiences.
Proactive mindset—identifies gaps, proposes solutions, and drives improvements without waiting for direction.
Curious and engaged with the evolution of ML, AI, and LLM technologies—you follow what's emerging and think about how it could apply to real problems.
Demonstrates a commitment to Breeze Airways' safety culture, values, and mission.

Responsibilities

Serve as the primary owner of how ML models move from experimentation to production, partnering closely with data scientists to turn notebooks and experiments into reliable, production-ready services and batch jobs.
Build reusable, well-tested frameworks, tools, and pipelines that enable faster and more consistent model development and deployment
Design and optimize training and inference systems for latency, throughput, reliability, and resource efficiency.
Own the behavior and performance monitoring of models in production to ensure stability, accuracy, and operational efficiency.
Develop APIs for real-time, latency-sensitive inference and orchestrate batch prediction pipelines that power internal business systems.
Work within our AWS and Snowflake environment to integrate models with production data flows, building on our existing development platform.
Stand up and maintain a model registry (MLflow, SageMaker Model Registry, or similar) to track experiments, versions, and deployments.
Define and implement clear versioning, validation, and promotion workflows for moving models from development to production.
Establish baseline monitoring for model quality, data drift, and prediction health.
Standardize packaging, testing, and deployment patterns so the Data Science team can ship models more consistently and confidently.
Collaborate closely with DevOps on CI/CD pipelines, infrastructure, and deployment processes, driving ML-specific requirements and best practices.
Create documentation, templates, and reference implementations that enable scalable, repeatable ML delivery.
As the team’s capabilities mature, help evaluate and productionize emerging model types, including LLM-based and agentic AI applications.