Senior Machine Learning Engineer

Key Skills

Algorithms, Amazon Simple Storage Service (S3), AWS, AWS Glue, AWS Sagemaker, Continuous Integration & Continuous Delivery - CI/CD, ETL frameworks, Machine learning techniques, Python Programming, Terraform

Job Description

Why Vacasa

We started with just one home and an idea: to bring homeowners and renters together with smart technology and caring local teams. Today, we’re the largest full-service vacation rental company in North America thanks to the people who give us their best every day. You’ll fit right in here if you’re curious, entrepreneurial, and thrive in a rapid-growth environment.

Why Engineering at Vacasa

We build the tools that allow other departments to succeed. We’re constantly experimenting and fine-tuning our products. We value stability, security, and scalability. Our favorite word is autonomy—we want everyone to have a voice.

What we’re looking for

As a Machine Sr Learning Engineer at Vacasa, you will join a nimble, cross-functional team of bright machine learning engineers and data scientists. This team has a high impact on company revenue and operational efficiency. You will productionize ML models in the cloud using Python, and adapt feature engineering techniques to both batch and real-time pipelines. This includes writing robust, maintainable, and reliable systems with validation, monitoring, metrics, for both internal and external customers. You will champion best practices for engineering and architecture, mentor up-and-coming talent, set an example of conduct through cross-team collaboration, communicate to stakeholders, balance engineering concerns with business needs, collaborate on initiatives, and lead mid-sized projects.

Vacasa’s machine learning and data science research is broad. We train dozens of models, from dynamic daily pricing for all units, to probability models that are used throughout the company. There’s potential to explore and implement recommender systems, NLP techniques, and neural networks. Vacasa has hundreds of millions of records for model training. Help us discover value in our data and bring it to customers!

What you'll do

• Productionize machine learning models using AWS, Python 3, CI/CD, and Terraform.
• Write maintainable, reliable, and robust pipelines complete with unit and integration tests.
• Contribute to all phases of development and delivery, including a weekly on-call rotation.
• Develop dashboards to monitor pipeline health, and alert on key metrics.
• Collaborate with a cross-functional team of engineers, QA, data scientists, and Product.
• Continually update your engineering skills using modern tools and techniques.
• Deliver results to stakeholders on time and within budget.
• Conduct code reviews, peer design, and demonstrate respectful, effective communication.
• Debug difficult problems across multiple projects, and become an expert in MLOps.
• Lead mid-sized projects, and balance best engineering practices with business needs.
• Co-lead large initiatives, architect systems from scratch, and mentor junior team members.

Skills you'll need

• 5+ years of software engineering, including 2+ years of machine learning engineering or equivalent experience.
• Familiarity with machine learning algorithms, including supervised and unsupervised.
• Ability to function in a “big data” environment such as Apache Spark.
• Strong Python experience.
• Experience writing infrastructure as code, Terraform is a plus.
• Familiarity with the AWS ecosystem and tools such as S3, Glue, or SageMaker.
• RDBMS and ETL experience, data warehouse experience is a plus.
• Ability to work in office 4 days / week with the option to work from home 1 day / week
• The benefit of a hybrid schedule requires a reliable internet connection and must meet a minimum of 50 mbps

What you’ll get

• Competitive salary
• Ability to participate in our Employee Stock Purchase Plan
• Paid vacation and holidays
• Meal vouchers
• Employee Assistance Program
• Career advancement opportunities
• Employee discounts
• All the equipment you’ll need to be successful
• Great colleagues and culture