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You’ll form a new team of passionate engineers dedicated to building and scaling AI/ML platforms and solutions that underpin Atlassian’s security, privacy, anti-abuse, and compliance posture. Trust Engineering operates as a platform and enablement team, partnering closely with Security, Privacy, Product Abuse, and GRC stakeholders, and leading the charge on AI safety and responsible AI for enterprise environments.
Job Responsibility:
Lead AI/ML Strategy for Trust: Drive the development and implementation of advanced machine learning algorithms and AI systems for Trust, Security, Product Abuse, and Compliance use cases (e.g., threat detection, vulnerability management, privacy automation, AI safety)
Architect and Scale ML Platforms: Design and build scalable, secure, and reliable ML infrastructure and pipelines, ensuring compliance with privacy and regulatory requirements
AI Safety and Responsible AI: Develop and champion AI safety practices, including output moderation, explainability, and alignment with evolving regulatory frameworks
Cross-Functional Collaboration: Partner with product, engineering, security, privacy, and analytics teams to deliver transformative AI/ML solutions that enhance Atlassian’s trust posture
Mentorship and Leadership: Mentor and guide ML engineers and data scientists, fostering a culture of technical excellence, innovation, and continuous improvement
Innovation and Research: Stay at the forefront of AI/ML research, evaluating and applying the latest techniques (e.g., LLMs, anomaly detection, privacy-preserving ML) to real-world Trust challenges
Platform Enablement: Build reusable ML services and APIs that empower other teams to integrate AI/ML into their products and workflows
Operational Excellence: Ensure high availability, reliability, and security of all ML-powered Trust platforms and services
Requirements:
Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Mathematics, or a related field, or equivalent practical experience
12+ years of industry experience in machine learning, data science, or AI, with a proven track record of delivering production-grade ML systems
Deep expertise in Python, Go, or Java, with the ability to write performant, production-quality code
familiarity with SQL, Spark, and cloud data environments (e.g., AWS, GCP, Databricks)
Experience building and scaling ML models for business-critical applications, ideally in security, privacy, anti-abuse, or compliance domains
Strong communication skills, able to explain complex ML concepts to diverse audiences and influence stakeholders
Demonstrated ability to solve ambiguous, complex problems and drive projects from ideation to production
Agile development mindset, with a focus on iterative improvement and business impact
Nice to have:
Experience in AI safety, responsible AI, or regulatory compliance for ML systems
Background in privacy engineering, anti-abuse, security automation, or GRC platforms
Experience developing deep learning models, LLMs, or privacy-preserving ML techniques
Experience mentoring and growing high-performing ML teams
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