Machine Learning Engineer, Salesforce

• 8+ years experience with machine learning and software engineering
• Put machine learning models, generative AI or other data-derived artifacts into production at scale, especially for text-based applications
• Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java
• Built with common ML frameworks like pytorch, Keras, XGBoost, Tensorflow or Scikit-learn
• An analytical and data driven approach, and know how to measure success with complicated ML/AI products
• Led technical architecture discussions and helped drive technical decisions within the team
• The ability to write understandable, testable code with an eye towards maintainability
• Strong communication skills and the ability to explain sophisticated technical concepts to designers, support, and other specialists
• Strong computer science fundamentals: data structures, algorithms, programming languages, distributed systems, and information retrieval
• A bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics or a related field, or equivalent training, fellowship, or work experience

Desirable

• As a Machine Learning Engineer you will craft and implement ML and generative AI powered features that leverage our data to make a fabulous, robust, safe, and valuable product for our users
• Our team has built out robust functionality spanning LLM deployment, evaluation, monitoring and quality improvements
• We are looking for an expert engineer who has worked in the development of both traditional ML and more recent generative AI solutions to help guide the architecture and development of AI
• Brainstorm with Product Managers, Designers and Engineers to conceptualize and build new features for our large (and growing!) user base
• Produce high-quality results by leading or contributing heavily to large multi-functional projects that have a significant impact on the business
• Help other engineers actively own features or systems and define their long-term health, while also improving the health of surrounding systems
• Collaborate with peers across Engineering to triage bugs and troubleshoot sophisticated production issues across the stack
• Mentor other engineers and deeply review code
• Improve engineering standards, tooling, and processes
• Design and deliver scalable RAG services that can be integrated with numerous applications, support thousands of tenants, and operate at scale in production
• Drive system efficiencies through automation, including capacity planning, configuration management, performance tuning, monitoring, and root cause analysis
• Participate in periodic on-call rotations and be available to resolve critical issues
• Collaborate with Product Managers, Application Architects, Data Scientists, and Deep Learning Researchers to understand customer requirements, design prototypes, and bring innovative technologies to production
• Engage in light-hearted yet thought-provoking conversations with your team around fun topics, such as: Should the popularity of penguins influence conservation efforts? Is creativity more like a neural network or a heuristic algorithm? How would society evolve if humans communicated through colors as well as words?