Description

Salesforce AI Research is looking for a Machine Learning Engineer to design and build cutting-edge AI/ML solutions powering enterprise AI products. In this role, you will play a key role across the lifecycle of ML components – from ideation to deployment – and work closely with research, engineering and product partners to turn innovative ideas into customer-facing features.

Learn more about Salesforce AI Research at https://www.salesforceairesearch.com - join us to transform research innovations into real customer impact.

Your typical day looks like this:

• Design, implement and evaluate LLM-based and classical ML systems for enterprise use cases.
• Transform research prototypes into AI/ML services that generalize across Salesforce customers at scale.
• Collaborate with fellow engineers, researchers and cross-functional partners to build data, model and deployment pipelines.
• Partner with product managers and stakeholders to understand customer needs and influence product direction.
• Lead technical discussions, drive prioritization and ensure timely delivery of ML features.
• Mentor junior engineers on technical design, implementation and engineering best practices.

Required Qualifications:

• MS or Ph.D. in a quantitative discipline with 3+ years of industry experience, or BS with 5+ years of relevant industry experience, or equivalent practical experience building ML production systems.
• Strong experience designing, training and evaluating machine learning models and working with large-scale datasets. Proficient in Python and ML frameworks (e.g., TensorFlow, PyTorch).
• Proven experience building scalable, production-grade machine learning services.
• Experience in one or more ML domains such as LLMs, NLP, deep learning, or classical machine learning.
• Experience applying ML techniques in real-world applications such as conversational AI, retrieval-augmented generation (RAG), synthetic data generation, content classification, or recommendation systems.
• Familiar with software engineering methodologies (agile, scrum) and best practices (version control, testing, CI/CD, code review, etc.).
• Strong communication skills, comfortable leading technical discussions and aligning project priorities across cross-functional partners such as engineers, researchers, and product managers.

Preferred Qualifications:

• Experience designing and building microservices, familiar with Kubernetes/Docker/RESTful API, etc.
• Experience with cloud platforms such as AWS/GCP/Azure.
• Knowledge of enterprise SaaS space.

For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.