AI Engineer (ML Systems)

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