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 Machine Learning at Vacasa
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 we’re looking for
As a Senior Machine Learning Engineer at Vacasa, you will be focused on both Data Science model development and the engineering to productionize models. 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 cost reduction.
You will develop and productionize ML models in the cloud using Python, PySpark and/or Scala, and adapt feature engineering techniques for batch and real-time pipelines. This includes writing robust, maintainable, and reliable systems with validation, monitoring, metrics, for both internal and external customers.
As a senior machine learning engineer, you will be expected to lead initiatives; define best practices for data science, engineering and architecture; mentor up-and-coming talent; set an example of conduct through cross-team collaboration; communicate to stakeholders; and balance engineering concerns with business needs. You will be accountable for prototyping and developing models; selecting technologies; and defining patterns that will be used across machine learning projects.
What you'll do
- Develop, prototype and productionize machine learning models through Spark, Sagemaker or similar distributed computing stacks
- Feature Engineer large amounts of data into features that can be used across models, using latest techniques including embeddings, PCA and MDS
- Design and architect scalable ML pipelines that stack and combine multiple model outputs to produce final downstream predictions for our consumers
- Develop ML pipelines in a maintainable, reliable, and robust manner complete with unit and integration tests using Airflow and Terraform (Kubeflow is a plus)
- 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.
- Conduct code reviews, peer design, and demonstrate respectful, effective communication.
- Debug difficult problems across multiple projects, and become an expert in MLOps.
- Lead large-scale initiatives, provide feedback, and mentor other team members.
- Define repeatable architectural patterns for large-scale, adaptive, secure, performant systems.
- Select technologies, guide technical solutions, and produce high quality documentation.
Skills you'll need
- 6+ years of software engineering, including 2+ years of machine learning engineering or equivalent experience.
- Demonstrated experience with implementing supervised/unsupervised machine learning models in production
- RDBMS and ETL experience, data warehouse experience.
- Ability to function in a “big data” environment such as Apache Spark.
- Familiarity with the AWS ecosystem and tools such as S3, Glue, or SageMaker.
- Strong Python experience. Scala is a plus
- Experience writing infrastructure as code, Terraform is a plus.
- Other duties, as assigned.
What you’ll get
- Health/dental/vision insurance - employee & family coverage options
- Employer Sponsored & Voluntary Supplemental Benefits
- 401K retirement savings plan with immediate 100% company match on the first 6% you contribute
- Health & Dependent Care Flexible Spending Accounts
- Flexible vacation time
- Paid sick days and holidays
- Paid parental leave after one year of tenure
- Employee Assistance Program
- Career advancement opportunities
- Employee discounts
- All the equipment you’ll need to be successful
- Great colleagues and culture
- Please visit our careers page to review our full benefits offerings