Want to build cutting-edge tooling for machine learning? Ever wondered how much and which compute resources are required to train machine learning models that can classify millions of photos and reviews? Or how to automate the ML infrastructure for rapid model training and connect those models to a variety of model serving platforms including ranking and streaming systems? We’re looking for remote ML infrastructure engineers who thrive on living at the intersection of machine learning, scalable infrastructure, and massive flows of data.
On the Core ML team, our mission is to build the machine learning platform to pursue Yelp’s top business initiatives. We build tools which help engineers develop and apply their ML models in light speed using the latest technology frameworks, such as Jupyter, Spark, Kubernetes, Kafka, and Cassandra. In many cases, we’ll contribute or drive open-source projects to help us achieve our mission, including ML model serialization and inference projects. We are also building tooling and developing processes to centralize data products and feature stores for analysts and ML needs.
Come work with and learn from our team that is full of a passionate and diverse group of engineers with years of experience spanning machine learning modeling to systems engineering. We communicate across the company, inputting ML-based needs and outputting efficient tooling and systems. As machine learning evolves, we continue to ride the wave of innovation by combining industry best practices and cutting-edge tooling to bolster Yelp’s machine learning platform. See a recent blog post giving an overview of our ML Platform.
What You Will Do:
Build the platform that powers Yelp’s top business initiatives with machine learning
Streamline and build new abstractions to support machine learning workflows
Collaborate with other teams on building centralized data & feature stores
Connect regularly with different internal groups for input on their ML infra and data needs
Gain expertise in cutting-edge machine learning infrastructure
Apply ML techniques to deliver models and data for high impact business problems
What it takes to Succeed:
A balanced interest in machine learning, infrastructure, and data products
Deep understanding of the programming languages and systems you’ve worked on
Familiarity with tooling, including Jupyter, Apache Spark, TensorFlow, Docker, Kubernetes, Flink, and Kafka
Minimum 2 years industry experience or an academic background in machine learning, data mining, or data infrastructure
Passion for architecting large systems with elegant interfaces that can scale easily
Excellent written and interpersonal communication skills
A team player who lives the Yelp Values and thrives in a diverse and inclusive work culture
What you'll get:
Full responsibility for projects from day one, an awesome team, and a dynamic work environment
Competitive salary with equity in the company, a pension scheme, and an optional employee stock purchase program
25 days paid holiday initially, rising to 29 with service
Private health insurance, including dental and vision
Regular 3-day Hackathons and weekly learning groups, always with interesting topics
Opportunities to participate in events and conferences throughout Europe and the US
£58 per month toward any exercise of your choice
Yelp values diversity. We’re proud to be an equal opportunity employer and consider qualified applicants without regard to Age, Disability, Gender Reassignment, Marriage or Civil Partnership, Pregnancy and Maternity, Race, Religion or Belief, Sex.
Note: Yelp does not accept agency resumes. Please do not forward resumes to any recruiting alias or employee. Yelp is not responsible for any fees related to unsolicited resume
Yelp connects people with great local businesses. Our users have contributed approximately 127 million cumulative reviews of almost every type of local business, from restaurants, boutiques and salons to dentists, mechanics, plumbers and more. These reviews are written by people using Yelp to share their everyday local business experiences, giving voice to consumers and bringing “word of mouth” online.