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Principal Software Engineer – Machine Learning Modeler
| Company | Salesforce |
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| Location | San Francisco, CA, USA |
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| Salary | $230800 – $334600 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Senior, Expert or higher |
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Requirements
- Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java.
- Built with common ML frameworks like pytorch, Tensorflow, Keras, XGBoost, or Scikit-learn
- Experience building batch data processing pipelines with tools like Apache Spark, Hadoop, EMR, Map Reduce, Airflow, Dagster, or Luigi.
- Worked on generative AI apps with Large Language Models and possibly fine tuned them
- An analytical and data driven mindset, and know how to measure success with complicated ML/AI products.
- Put machine learning models or other data-derived artifacts into production at scale.
- 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 you are capable of explaining complex technical concepts to designers, support, and other specialists.
- Strong computer science fundamentals: data structures, algorithms, programming languages, distributed systems, and information retrieval.
- A related technical degree required
Responsibilities
- Develop ML models supporting ranking, retrieval, and generative AI use-cases.
- Brainstorm with Product Managers, Designers and Frontend 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.
- Actively own features or systems and define their long-term health, while also improving the health of surrounding systems.
- Support in the development of sustainable data collection pipelines and management of ML features.
- Assist our skilled support team and operations team in triaging and resolving production issues.
- Mentor other engineers and deeply review code.
- Improve engineering standards, tooling, and processes.
Preferred Qualifications
- Expertise in retrieval systems and search algorithms.
- Familiarity with vector databases and embeddings.
- Knowledge of using multiple data types in RAG solutions including structured, unstructured, and knowledge graphs.
- Broad experience across NLP, ML, and Generative AI capabilities.
