About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword — it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Responsibilities
- Working with Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies on a modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies
- Experience building Big Data services on AWS, GCP or other public cloud substrates
- Eat, sleep, and breathe services. You have experience balancing live-site management, feature delivery, and retirement of technical debt
- Partner with Product Managers, Architects and Data Scientists to understand customer requirements, and help translate requirements to working software
- Own the technology for fully orchestrated machine learning APIs for Einstein Platform
- Contribute to the long-range plan, and help drive the microservices architectures for machine learning
- Designing, developing, debugging, and operating resilient distributed systems that run across thousands of compute nodes in multiple datacenters
- Participate in the teams on- call rotation to address complex problems in real-time and keep services operational and highly available
- Create and enforce processes that ensure quality of work, and drive engineering excellence
- Exhibit a customer-first mentality while making decisions, and be responsible and accountable for the output of the team
- Partner with vendors like AWS and Data Science teams to pick best fit in terms of libraries and compute to deliver cost effective and scalable model hosting and tuning/training capabilities
Requirements
- BS, MS, or PhD in computer science or a related field, or equivalent work experience
- 5+ years of hands-on experience with big data, machine learning, and microservices architectures
- Track record of leading highly impactful projects from conception to finish
- Expertise in JVM based languages (Java, Scala) and Python
- Experience leading/working in teams that have built and and run machine learning services, such as for training & inferences, at scale for predictive and generative models
- Experience with open source projects such as Spark, Kafka, Feast, Iceberg
- Experience in building software on AWS cloud computing such as OpenSearch, DynamoDB, EMR and S3
Preferred Qualifications
- Experience working in machine learning, and technologies such as Amazon SageMaker and Google Cloud ML
- Experience building or leading teams that have built and and run real-time data applications in production