Unprecedented value from sensitive data. mathematically proven privacy for the enterprise
LeapYear's secure machine learning platform is deployed by some of the largest enterprises in the world across finance, healthcare, and technology.
Our technology ensures differential privacy, a widely recognized standard of data privacy that enables all data - including sensitive information - to be utilized for analytics, while providing mathematically proven privacy protection.
The LeapYear system is composed of a core set of components that allow private machine learning on data sets that can scale to petabytes. The core includes private algorithms for relational operations, statistical methods and machine learning. A data scientist accesses private data using a Python API. The system includes services for authentication, access control, logging, auditing and support for integration of data from a variety of data sources including SQL/NoSQL Databases, HDFS and S3. Queries are processed using Spark to support to enable fast, distributed processing of massive datasets. Administration is provided via a web-based GUI or an API.
As one of LeapYear's first field engineers, you will be responsible for the deployment of LeapYear's machine learning platform into complex customer environments. LeapYear works with some of the largest enterprises across healthcare, financial services, and technology, and enables full utilization of previously inaccessible data. We need technical field engineers who are comfortable deploying complex systems and thinking quickly on their feet to resolve customer issues.
For details on the specific responsibilities and requirements of this role, please see below.
- Own the process for deploying and upgrading the LeapYear platform in customer environments
- Own reference architectures and network topology diagrams across customers
- Scope, conduct, and document experimental deployments for our evolving machine learning platform running on Spark
- Help our customers resolve critical issues related to software evaluation and go-live production deployment
- Provide structured feedback to and work closely with the Engineering team building the LeapYear platform.
- Contribute to continuous improvement, scalability, and automation of our deployment practices
- Build our internal knowledge base, as well as tools and infrastructure for LeapYear's team of customer-facing data scientists and solutions architects
- Customer-facing experience deploying and integrating enterprise software
- Experience analyzing and integrating with customer's existing systems (e.g. authentication, monitoring, logging)
- At least two years experience with Hadoop or Spark, in either an administrative, development, or support role
- Strong Linux/Unix administration skills
- Prior experience troubleshooting production issues, staying calm under pressure
- Effective written and verbal communication skills. Comfortable interfacing with customer personnel from sysadmins to technical IT leaders.
- Hands-on experience deploying and maintaining systems in AWS/GCP/Azure (AWS preferred)
- Broad knowledge of enterprise architecture, networking, databases. Interest in data science and machine learning
A Few of the Perks
- Culture of teaching and learning
- Competitive compensation package of salary and equity
- Catered lunch every day
- Company outings
- Build your ideal work station
- Generous health insurance plan
- Relocation support and visa sponsorship
Health, dental, and vision coverage for you and your family , FSAs for health and dependent care, Disability insurance (Long and short-term)
401k plan with match
Early-stage stock options.
Flexible schedule, work-from-home Wednesdays
Great snacks and catered meals every day
Good ergonomics (standing desks, nice chairs and monitors), monthly hikes, bike rides, game nights, happy hours, and more
LeapYear Technologies at a glance
LeapYear Technologies focuses on Enterprise Software, Security, and Machine Learning. They have a small team that's between 11-50 employees. To date, LeapYear Technologies has raised $38.2M of funding; their latest round was closed on May 2019.