Software Engineer, Salesforce

• 3+ years of hands-on experience with Big Data, machine learning, and microservices architectures
• Proven experience leading highly impactful projects, from inception to delivery, with a focus on large-scale machine learning services (e.g., training & inference pipelines, predictive and generative models)
• Strong programming expertise in JVM-based languages (Java, Scala) and Python
• Experience with distributed systems and cloud platforms like AWS, GCP, or Azure, including services like OpenSearch, DynamoDB, EMR, S3, and Google Cloud ML
• Expertise with open-source projects and technologies such as Spark, Kafka, Feast, and Iceberg
• Solid understanding of containerization (e.g., Docker, Kubernetes) and cloud-native deployment patterns

Desirable

• At Salesforce, we are reimagining how organizations build and deploy advanced AI and machine learning products!
• Our Einstein platform is at the forefront of democratizing Generative AI, Predictive AI, and AI Agents, enabling businesses to rapidly create, deploy, and manage intelligent applications
• By seamlessly integrating large language models (LLMs), AI Agents, and Predictive AI across all Salesforce clouds, we empower customers to transform their workflows, applications, and customer experiences with innovative, AI-driven capabilities
• Design & Lead High-Scale Machine Learning Services: Work with pioneering technologies like Sagemaker, TensorFlow, PyTorch, Triton, and Spark to build scalable, distributed machine learning services on a modern containerized stack using Kubernetes and Spinnaker
• Drive Microservices Architecture: Lead the design, development, and ongoing optimization of microservices architectures for machine learning pipelines, APIs, and model management systems
• Ownership of Core Technology: Take ownership of core platform technologies related to orchestrated machine learning APIs, from design to delivery, driving continuous improvement and scalability of the platform
• Lead High-Impact Engineering Initiatives: Take the lead on projects that significantly impact product growth, applying your expertise to mentor engineers and make strategic technical decisions
• Maintain Service Reliability: Balance live-site management with the delivery of new features and technical debt resolution. Participate in on-call rotations to resolve complex, real-time issues and ensure high availability of services
• Build and Scale Data Services: Work with AWS, GCP, and other cloud providers to build and scale Big Data and real-time data applications
• Contribute to the long-term technical roadmap and strategy for the Einstein Platform team
• Own technical initiatives that push the boundaries of machine learning and AI at Salesforce, influencing key design decisions and product features
• Lead by example in technical execution, mentoring and coaching team members to tackle complex challenges and grow in their roles
• Drive end-to-end ownership of critical AI features, from the architecture and design phase through to deployment, maintenance, and scaling