AI/ML Job: Machine Learning Engineering and Data Science Engineering Roles

Salesforce

Machine Learning Engineering and Data Science Engineering Roles at Salesforce

🇺🇸 United States › California › San Francisco
  (Posted Feb 18 2021)

Do they allow remote work?
Remote work is possible, see the description below for more information.

Job description
Locations:
California - Remote
California - Palo Alto
California - San Francisco
Massachusetts - Remote
Massachusetts - Boston
Massachusetts - Burlington
Massachusetts - Cambridge
Massachusetts - Somerville
Washington - Bellevue

Machine Learning Engineering and Data Science Engineering Roles

We're hiring across teams, which include: Analytics, Commerce, Einstein, IoT, Mulesoft, Search Relevance, Security, Service, Service Protection team, Marketing Cloud

Salesforce is looking for an exceptional engineer ideally with a dual background in machine learning and software engineering to help us take on one of the world’s most extensive data sets and transform it into amazing products that feel like magic. You will work on cutting-edge AI applications and products. Brainstorming data product ideas with data scientists and engineers to build data products used by hundreds of millions people every day.

Depending on the team, responsibilities may include:
Developing data infrastructure that ingest and transforms data from different sources and customers at scale.
Building performant and scalable machine learning infrastructure to support data science needs for Clouds and across hundreds of thousands of Salesforce customers.
Design, develop, bring to production at a large scale and support “intelligence” features on a world-class search service that serves millions of requests daily on a diverse corpus of data including structured, unstructured and social feeds.
Weighing different architectural approaches in a way that balances data science flexibility with time to market, maintainability, cost, and scalability constraints.
Develop new relevance features and techniques build upon the latest results from the research community.

What we care about:
You have industry experience with writing production level code (e.g., Python, Golang, Scala, PySpark, Java) and taking ML models/ algorithms to production. We develop real products and you need to be an expert in coding.
Preference for 5+ years of industry experience (without PhD); at least 4+ years of industry experience with PhD. This is not an entry level / new college graduate role.
Self-starter who can see the big picture, and prioritize their work to make the largest impact on the business’ and customer’s vision and requirements.
Excellent communication & leadership.
We prioritize professional industry experience; advanced degrees alone do not replace real world experience.
We have massive scale. You need to have experience in distributed, scalable systems. Consistency / availability tradeoffs are made here. You’ve tinkered with modern data storage, messaging, and processing tools (Kafka, Spark, Hadoop, Cassandra, etc.) and demonstrated experience designing and coding in big-data components such as HBase, DynamoDB, or similar.

Required Qualifications:
At least 4 years of hands-on professional industry experience in engineering positions focused on Machine Learning, Information Retrieval, Recommendation systems or Data Mining, Natural Language Processing, Learning to Rank.
Strong programming skills in Python, Java, Golang, or Scala.
Strong knowledge of Object Oriented design, advanced algorithms, data structures, design patterns, etc.
Experience with building machine learning serving infrastructure, writing production level code, and deploying Machine Learning models to production.

Preferred Qualifications:
Master’s or PhD in a relevant field and/or experience in any of the following is highly regarded: Computer Science, Machine Learning, Data Science.
Experience working with machine learning libraries such as TensorFlow, PyTorch, etc.
Experience with Agile software development and Test Driven Development methodologies.
Experience building data pipelines and data infrastructure that ingests and transforms data from different sources and customers at scale.
Experience building Software as a Service (SaaS) applications, multi-tenancy, and micro-services architecture.


 

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