• Want to apply AI to real projects with direct business impact, instead of just POCs that never reach production? • We are looking for a Senior Artificial Intelligence / Machine Learning Engineer to lead cutting-edge solutions in a modern, data-driven, 100% remote environment. • You will be part of a small, autonomous team with direct contact with product and the freedom to propose architectures and technologies. • Design, train, and put ML/Deep Learning models into production. • Build data and MLOps pipelines (training, versioning, monitoring). • Work side by side with Product and Engineering to understand problems and metrics. • Rapidly prototype and evolve solutions into scalable production systems. • Optimize model performance, cost, and quality in the cloud. • Drive AI engineering best practices within the team (code review, standards, testing). • Explore and apply LLMs, NLP, and generative models where appropriate.

As a Principal Machine Learning Engineer, you will drive the development and implementation of cutting-edge machine learning algorithms, training sophisticated models, and collaborating with product, engineering, and analytics teams to build AI functionalities into Atlassian products and services. Your daily responsibilities will encompass a broad spectrum of tasks - designing system and model architectures, conducting rigorous experimentation and model evaluations, and providing guidance to emerging ML engineers. Your role is pivotal, ensuring AI's transformative potential is realized across our offerings. Technical Leadership & Execution - Drive complex decisions that impact the work of teams and change their technical direction over multiple quarters - Regularly tackle the largest and most complex problems on the team, from technical design to launch - Set the direction of systems and capabilities, balancing progress over perfection - Determine plans-of-attack on large projects and solve complex architecture challenges Model Development & Experimentation - Design, develop, and deploy production-grade ML models (e.g., ranking, retrieval, LLM-based systems) to optimize user experience and achieve business objectives - Conduct meticulous experimentation and model evaluations, backing decisions with data - Develop robust feature engineering practices to ingest, process, and serve features for offline training and online inference at scale - Oversee end-to-end deployment of ML solutions into production, ensuring continuous evaluation, monitoring, and improvement Cross-Functional Collaboration - Collaborate closely with product managers, designers, and engineering teams to integrate AI/ML capabilities into products - Partner across engineering teams to take on company-wide programs spanning multiple projects - Communicate complex technical concepts clearly to both technical and non-technical stakeholders Mentorship & Organizational Impact - Mentor and guide junior and senior engineers, fostering a culture of innovation, collaboration, and continuous learning - Actively share knowledge and expertise through mentoring and coaching beyond direct reports - Contribute to programs of work that scale across the department - Identify, solve, and bridge gaps/problems across teams using experience and expertise Decision Making & Direction - Quickly collate and analyze key decision parameters, balancing speed, risk, and impact appropriately - Limit ambiguity and risk by experimenting and prototyping - Understand how contributions of multiple capabilities fit into larger products and platforms At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range. Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. This role may also be eligible for benefits, bonuses, commissions, and equity. In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are: Zone A: $206,100 - $269,075 Zone B: $186,300 - $243,225 Zone C: $171,900 - $224,425 Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh .

• Contribute to RL frameworks that drive the design-make-test-analyze (DMTA) cycles that power our EMMI platform, which coordinates a closed-loop between a highly automated lab and our reward models. • Develop synthetic data engines and the inference infrastructure needed to simulate environments for large-scale training. • Maintain rigorous evaluations to continually monitor the performance of learned policies, using large proprietary datasets collected from internal programs.

• Extend structure informed generative models of potency to causal models of in-vivo molecular interactions • Experimentally realize improvements in selectivity, generality, scope, and/or accuracy of intermolecular interactions designed with ML models • Collaborate with computational and experimental teams