AI Engineer, ML Systems

Salesforce • San Francisco, CA

1d

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. We are seeking a Lead AI Engineer, ML Systems to join the Salesforce AI Research Incubation Team. In this role, you will own the engineering systems that power model inference, fine-tuning, and evaluation, enabling research models to be reliably deployed and evolved in production environments. You will work closely with AI researchers, agent engineers, and platform teams to support model serving, LoRA-based fine-tuning workflows, and model lifecycle management. This role focuses on production ML systems, not on inventing new model architectures. This is a lead-level individual contributor role with deep ownership of model-facing systems and strong cross-team influence.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, or a related field.
• 5+ years of experience in software engineering, with significant ownership of backend or distributed systems.
• Strong proficiency in Python, with experience building production services.
• Hands-on experience with AI/ML model serving, inference pipelines, or ML systems engineering.
• Experience designing reliable, scalable systems for production environments.
• Familiarity with cloud platforms (AWS, GCP) and containerized environments (Docker, Kubernetes).
• Strong debugging skills across system, data, and model-facing failures.
• Excellent communication skills and ability to collaborate across research and engineering teams.

Nice To Haves

• Experience with fine-tuning techniques such as LoRA or PEFT.
• Familiarity with model evaluation frameworks and regression testing.
• Experience with GPU-based workloads or ML infrastructure.
• Knowledge of data formats and pipelines commonly used in ML systems.
• Prior experience working closely with AI research or incubation teams.

Responsibilities

• Design, build, and maintain model inference and serving systems, including integration with AI gateways.
• Own and evolve fine-tuning pipelines (e.g., LoRA / PEFT) using internal model tooling.
• Develop and maintain model evaluation, regression detection, and rollout workflows.
• Collaborate with AI researchers to transition research models into production-ready assets.
• Optimize inference systems for latency, throughput, stability, and cost efficiency.
• Implement best practices for model versioning, deployment, rollback, and monitoring.
• Partner with agent and platform engineers to ensure smooth integration between model systems and agent runtimes.
• Provide technical leadership and mentorship on ML system design and operational excellence.

Benefits

• Own the systems that turn research models into production AI capabilities.
• Work at the intersection of AI research and large-scale engineering systems.
• Shape how models are trained, deployed, evaluated, and evolved.
• Competitive compensation, benefits, and strong long-term growth opportunities.
• time off programs
• medical
• dental
• vision
• mental health support
• paid parental leave
• life and disability insurance
• 401(k)
• an employee stock purchasing program

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level