Sr. Director - AI Engineering

Salesforce•Seattle, NY

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. Salesforce’s AI Foundations team is foundational to enabling both traditional machine learning applications and AI agents at enterprise scale. This team empowers the Digital Enterprise Technology organization to build, deploy, evaluate, and operate trusted AI systems in days instead of months. As Senior Director of Engineering, you will provide unified technical leadership across the AI foundations platform stack—spanning ML infrastructure, agent lifecycle management, evaluation, observability, and governance. You will drive interoperability across platform components, operationalize a data mesh–oriented architecture, and ensure the platform delivers high reliability, strong security, and cost-efficient scalability. This role is central to aligning platform capabilities with strategic business priorities, enabling safe and rapid adoption of generative AI and predictive AI across the enterprise.

Requirements

15+ years of engineering experience , with 7+ years leading platform or infrastructure teams in ML, data, or AI-heavy environments.
A master's or Ph.D. degree in computer science, machine learning, artificial intelligence, or equivalent industry experience.
Proven experience with ML and platform infrastructure , including Kubernetes-based systems, CI/CD, distributed systems, and observability stacks (metrics, logs, tracing).
Expertise in generative AI, ML algorithms, and frameworks such as Hugging Face, Tensorflow, PyTorch, etc.
Experience with cloud platforms (e.g., AWS, GCP) and distributed computing frameworks (e.g., Spark, Hadoop).
Hands-on familiarity with experimentation frameworks , such as A/B testing, canaries, and shadow deployments, and integrating experiments into ML/agent pipelines.
Experience building evaluation systems for models and agents, including offline tests, regression suites, online monitoring, and LLM-as-a-Judge–style approaches.
Strong background in AI agents and LLM systems , including tool use, multi-step workflows, RAG, prompt and policy management, and common agent failure modes.
Experience with data and ML platforms (e.g., Snowflake-centric workflows, feature stores, training pipelines).

Responsibilities

Lead the engineering of AI Foundations team that enables teams to build, deploy, evaluate, experiment on, monitor, and govern AI agents and ML models safely and at enterprise scale.
Own three core platform areas ML Platform & Developer Productivity (training, inference, environments, cost/perf) Model & Agent Lifecycle & Governance (CI/CD, registries, lineage, access control) Agent Observability, Evaluation & Reliability (quality, drift, experimentation)
Make agent evaluation and experimentation default platform capabilities , ensuring every production agent and model ships with: Offline evaluation (golden scenarios, regression suites) Pre-deployment quality gates in CI/CD Controlled experimentation (A/B tests, canaries, shadow traffic) Continuous post-deployment monitoring
Drive end-to-end observability across data pipelines, retrieval, model inference, tool execution, and agent outcomes, with clear SLIs/SLOs for quality, latency, reliability, and cost.
Standardize ML and agent development workflows , reducing time-to-production and eliminating bespoke infrastructure across teams.
Partner cross-functionally with Applied AI, Data Science, Product, Security, Legal, and Responsible AI to translate business and regulatory requirements into enforceable engineering systems.
Build and lead a high-performing organization of engineering managers and senior engineers, setting a strong technical bar and culture of operational excellence.