Lead Software Engineer, Salesforce

• Bachelor’s degree in Computer Science and 8+ years of experience, or equivalent industry experience
• Deep knowledge of two or more functional or scripting programming languages: Python, Scala, or equivalent
• Experience operating time-series forecasting, anomaly detection and AI integrated products in production environments
• Extensive experience working with Data Scientists and operating ML models in production services
• Understanding of Data Science, Machine Learning and AI concepts
• Extensive experience with distributed services (REST, rpc or similar APIs) and relational databases (Postgres or similar)
• Experience with orchestration and workflow management tools, i.e. Airflow
• Experience with distributed compute platforms like Trino or Spark
• Experience with Agile development methodology, Test-Driven Development, incremental delivery, and CI/CD
• Experience owning and operating services throughout the software development lifecycle including design, development, release and maintenance
• Experience communicating technical vision, mentoring junior engineers and managing projects

Desirable

• The Salesforce Cloud Economics and Capacity Management (CECM) team is looking for a Lead Engineer with experience developing AI and ML powered applications to join us!
• You will be working cross-functionally with engineers, architects, and product managers to build the breakthrough features that our internal customers will love, adopt and use while ensuring stable and scalable applications
• You will also be working with data scientists to innovate and deliver distributed backend technologies, sometimes including big data technologies
• You'll be a part of a modern, lean, self-governing product engineering team where you have the ability to switch hats between coding to requirements gathering, to testing for quality and performance
• CECM develops intelligent, data driven tools leveraging ML and AI techniques for Forecasting, Anomaly Detection, and LLM which enable strategic decision-making pertaining to Salesforce infrastructure expenditure and capacity management
• We are building a platform that provides near real-time monitoring of cost and capacity utilization of the infrastructure, which will help in optimizing resource allocation and minimizing costs
• We apply advanced machine learning techniques to turn the petabytes of data generated by our global infrastructure into actionable predictions and business insights used by capacity planners, internal service owners, and technical leaders daily
• As an internal tooling team, engineers are expected to directly interact with customers to develop requirements and design, release and maintain distributed systems with visibility throughout Salesforce
• This is a fantastic opportunity for someone who is passionate about building scalable, resilient, distributed systems that collect, process, and analyze massive volumes of operational data
• The technology stack includes Python, Airflow, K8S, Spark, Presto, Airflow, Postgres, and microservices within cloud native environments
• ML and AI applications include time-series forecasting, anomaly detection and AI integrated products
• We are looking for a Lead Engineer who can drive our strategy for optimizing cost efficiency and improving utilization across all of the Salesforce services
• You will not only provide visibility into opportunities to improve availability, but also work with multiple service owners to help and guide delivering on those optimizations
• Drive capacity visibility and automation improvements across multiple services at Salesforce
• Lead software development being delivered by multiple engineers
• Lead and participate in requirement gathering, design, and development of complex systems
• Independently design and deliver analytics tools and frameworks for diverse users, including other engineers, data scientists, and domain experts
• Mentor team members in all aspects of the software development lifecycle
• Master our code base, then improve it
• Build resilient, automated systems and assessing and integrating best-in-class technologies when appropriate