This is a senior leadership role focused on technical excellence and innovation. You will mentor a talented team of engineers and scientists, guide the end to end technical strategy from innovative research to production deployment and ultimately define the art of the possible for our most critical operational systems.
Responsibilities:
Lead the design and architecture of complex, end to end AI systems, guiding projects from initial conception and research through to production deployment and impact analysis.
Own the technical vision for the next generation of intelligent automation, creating patented technologies that directly impact Salesforce Field Service's core competitive advantage and market differentiation.
Provide technical leadership and set the strategic direction for the research and development of novel optimization and machine learning solutions.
Drive fast paced experimentation by independently or collaboratively prototyping solutions that showcase how new AI technologies can address real world business challenges. Collaborate with cross functional teams to translate prototypes into actionable solutions and define incremental delivery plans for implementation.
Mentor and guide a team of AI developers and scientists, fostering a culture of innovation, technical excellence, and continuous learning.
Stay at the forefront of academic and industry advancements by attending top tier conferences, networking with experts, and continuously learning about the latest in optimization, AI, and large language models (LLMs) to identify, evaluate, and apply emerging techniques that drive a significant competitive advantage.
JR314441
Requirements: Proven expertise in both classical optimization and machine learning, with a significant track record of blending solvers and models to solve complex industrial problems.
Deep experience and theoretical knowledge in one or more of the following areas: large-scale forecasting, reinforcement learning, or data-driven decision systems.
Hands-on proficiency in modern ML frameworks like PyTorch or TensorFlow and a strong proficiency of the data science ecosystem (e.g., Scikit-learn[sklearn]). Deep familiarity with modeling and forecasting time-series data is essential (e.g., ARIMA, Prophet, LSTMs).
Strong understanding of probabilistic modeling, statistical evaluation, and the use of simulation-based testing to rigorously validate complex models and decision systems in an offline environment.
Demonstrated experience architecting and deploying production ML systems. You can design robust, end-to-end pipelinesfrom data ingestion to model servingand have experience with core MLOps tooling (e.g., MLflow, Airflow, Docker/Kubernetes).
Ability to provide strong technical leadership and mentorship to a team of engineers, setting a high standard for technical excellence, innovation, and sound engineering practices.
This position is open to all candidates.