Full Job Description
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Job Category
Software Engineering
Job Details
We are a foundation machine learning platform team within the Trust Intelligence Platform organization with a main focus to build and accelerate scalable and resilient machine learning pipelines across the security engineering organization.
We are seeking a highly motivated, hands-on lead machine learning engineer with strong business understanding to define and execute the technical ML strategy. This role involves full lifecycle development and optimization of ML pipelines, with a strong focus on MLOps, infrastructure-as-code, CI/CD, and thorough monitoring. The lead will manage multiple ML pipelines, work closely with cross-functional teams, mentor others, and requires deep expertise in various ML technologies to deliver measurable business impact within a security and compliance framework. A critical component of this role is the proven ability to productize Generative AI solutions and agentic workflows to actively enhance the security posture and resilience for the organization and all Salesforce customers.
Your impact:
Define and drive the technical ML strategy, emphasizing robust and performant model architectures and MLOps practices.
Lead the end-to-end development of ML pipelines, focusing on automated retraining workflows and model optimization for cost and performance.
Own a portfolio of multiple machine learning pipelines within security and compliance.
Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation with a focus on model monitoring and drift detection.
Design and implement comprehensive monitoring solutions for model performance, data quality, and system health.
Collaborate with Data Science, Data Engineering, Research, and Product Management teams to deliver scalable ML solutions with measurable impact.
Provide technical leadership in ML engineering best practices and mentor junior machine learning engineers.
Required skills:
Masters or PhD in a quantitative field
Extensive experience (6+ years) in the end-to-end lifecycle of multi-model machine learning systems, from design and development to large-scale deployment.
Deep understanding and practical application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices.
Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring.
Solid foundation in feature engineering techniques and the implementation of feature stores.
Significant experience in developing and deploying generative AI solutions into production environments.
Expertise in infrastructure-as-code principles, monitoring tools, and big data technologies (Spark, Snowflake).
Experience in formulating ML governance policies and ensuring adherence to data security regulations.
Successfully led machine learning initiatives, consistently delivering significant and quantifiable business outcomes.
Exceptional collaboration abilities, with a strong capacity to work effectively across Data Science, Platform Engineering, Research and Product teams.
Preferred skills:
Expertise in advanced Natural Language Processing (NLP) methodologies.
Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams.
Previous experience in a mentoring role for junior engineers.
Track record of publications and/or patents in quantitative disciplines.
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