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

Role Description Inference is growing fast — and so is the volume of contributions, increasingly authored with the help of AI agents. That's a great problem to have, but it's outpacing our ability to keep quality high and cut releases on a predictable cadence. Today we ship roughly weekly, and it's a fight. We want to flip that equation. The goal is to build and continuously evolve an agentic-driven contribution and release pipeline — automated and semi-automated review, triage, CI/CD, and end-to-end testing — so that we can safely absorb a high volume of agent-generated PRs while staying firmly in control of quality. The ideal end state: nightly end-to-end tests across every target (both standalone and on-platform), backed by a growing, world-grounded suite that validates the real health of every build. With that foundation, daily releases become routine, and we can say "yes" to far more contributions without ever lowering the bar — pushing back, by design, according to strictly defined review standards. Alongside that, this person becomes the human face of inference: teaching internal teams and customers how to get more out of it, partnering with marketing to tell its story, and owning the (genuinely fun) work of bringing new models into the engine. Qualifications - 5+ years of hands-on experience building and operating production-grade ML systems, ideally involving large-scale deployment of modern AI models. - A real CV/ML foundation — understanding how computer vision models work internally and how to adapt them for real-world, high-impact use. - Stellar agentic skills — fluently building with AI coding agents and automating the engineering process itself. - Strong CS and systems background, with the ability to independently tackle complex programming, architecture, and reliability challenges. - Hands-on experience with CI/CD, release engineering, and test infrastructure. - Practical expertise with core ML technologies, including PyTorch, TensorFlow, ONNX, TensorRT, vLLM, etc. - Strong proficiency in image and video processing, including OpenCV, DeepStream, Pillow, PyAV, etc. - Excellent communication and soft skills — able to teach, write clearly, and collaborate across teams. - Open source maintenance experience is a strong plus. Requirements - Build and maintain inference, our flagship open source and commercial CV inference engine. - Build an agentic-driven contribution pipeline — automated and semi-automated review, triage, and CI/CD. - Design and grow a world-grounded, ever-expanding test suite that validates real build health across every target. - Define and enforce the "rules of the road" — the review standards and skills that agents and contributors must follow. - Streamline how new models get added to inference. - Teach and enable internal teams and customers. - Be the bridge between core engineering and clients — translating new capabilities into docs, demos, stories, and launches. - Contribute to and grow the broader open source community around the project. Benefits - $4000/yr Travel Stipend to travel anywhere anytime to work alongside other Roboflowers. - $350/mo Productivity stipend for enhancing work environment. - $350/mo AI Tools stipend. - Cover up to 100% of your health insurance costs for you and your partner or family. - $150/mo team lunch stipend. - Remote first/flexible schedule allowing collaborative work. - Unlimited PTO with an annual 2 week minimum. - 12 weeks parental leave. - Equity in the company. Interview Process - Before the Interview: Review of application, LinkedIn, Github, etc. - Introduction Phase: Technical Assessment [15m]. - Team Interview Phase: - Live coding [45m]. - Home assignment [30m]. - Meet with Inference Core team member [60m]. - Meet with hiring manager. - Final Interview Stage: - Meet with Head of Operations for a culture discussion [45m]. - Meet with CEO [30m].