Staff Machine Learning Engineer- Machine Learning Platform
Staff Machine Learning Engineer- Machine Learning Platform
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Job Description
Visa is a world leader in digital payments facilitating more than 215 billion payments transactions between consumers merchants financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative convenient reliable and secure payments network enabling individuals businesses and economies to thrive.
When you join Visa you join a culture of purpose and belonging where your growth is priority your identity is embraced and the work you do matters. We believe that economies that include everyone everywhere uplift everyone everywhere. Your work will have a direct impact on billions of people around the world helping unlock financial access to enable the future of money movement.
This opportunity is in Visas Machine Learning Platform. The Machine Learning Platform provides soft infrastructure support to Visas data scientists and researchers. We enable the training of statistical and machine learning models via development and maintenance of a distributed computing stack. Frameworks used in the stack are: Kubernetes Ray Torch Tensorflow XGBoost and Spark. Development is primarily in Python and Go.
We are a crossfunctional team that interfaces both with internal data science and research clients as well as other hard and soft infrastructure teams.
Responsibilities for this role include:
Monitoring of infrastructure health and problem solving to address persistent or urgent issues
Platform stabilization including unit and integration testing
Interfacing with Kubernetes and data platform teams
Implementation of new infrastructure features
This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.
Qualifications :
Basic Qualification:
- 5 years of relevant work experience with a bachelors degree or At least 2 years of work experience with an Advanced degree (e.g. Masters MBA JD MD) or 0 years of work experience with a PhD.
Preferred Qualifications:
- 6 or more years of work experience with a Bachelors Degree or more than 3 years of work experience with an Advanced Degree (e.g. Masters MBA JD MD).
- Masters degree or PH.D in computer science data science statistics or related field highly preferred.
- Proficiency in Python.
- Experience training ML models.
- Experience with Conda highly preferred.
- Experience with Ray highly preferred.
Additional Information :
Work Hours: Varies upon the needs of the department.
Travel Requirements: This position requires travel 510 of the time.
Mental/Physical Requirements: This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk communicate in person and by telephone frequently operate standard office equipment such as telephones and computers.
Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race color religion sex national origin sexual orientation gender identity disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
Visa will consider for employment qualified applicants with criminal histories in a manner consistent with applicable local law including the requirements of Article 49 of the San Francisco Police Code.
U.S. APPLICANTS ONLY: The estimated salary range for a new hire into this position is $139800.00 to $202750.00 USD per year which may include potential sales incentive payments (if applicable). Salary may vary depending on jobrelated factors which may include knowledge skills experience and location. In addition this position may be eligible for bonus and equity. Visa has a comprehensive benefits package for which this position may be eligible that includes Medical Dental Vision 401 (k) FSA/HSA Life Insurance Paid Time Off and Wellness Program.
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
