• Lead the MLOps strategy on Databricks, defining architecture and standards; • Design and review CI/CD pipelines for ML models (training, packaging, deployment, rollback); • Define practices for model versioning and registry, feature stores, experiment reproducibility, and serving; • Establish end-to-end observability: performance, data/model drift, data quality, and business metrics; • Review PRs and critical architecture decisions; mentor the squad on the most complex topics; • Ensure governance, security, and cost control of the data and AI platform; • Participate in key ceremonies (planning, technical refinements, checkpoints), optimized for a part-time workload.

• Support in the development of AI systems. • Optimize Python code for data pipelines. • Perform data analysis and validation using SQL. • Assist with data modeling and automation.

Your Future Team - DevAI Atlassian's journey began with Jira, a tool designed to help developers track bugs and manage their work. Over the years, Atlassian has expanded its suite of productivity tools, yet developers remain at the heart of our focus. We are revitalizing our product lineup to better serve this core audience. Atlassian's ecosystem uniquely positions us to enhance every step of the developers' productivity cycle. With DevAI now established, we continue to evolve our AI-native SDLC solution, deeply integrating AI to redefine developer experiences. Our products include code reviewers, code generation, agentic engineering, spec-driven development, and more. Our vision is to transform software development, and we have ambitious plans to achieve this. What You'll Do As part of our team, you will play a crucial role in innovating the core intelligence solutions within our DevAI products. Your responsibilities will include (but not limited to) LLM integration, prompt engineering, and developing end-to-end AI features using cutting-edge technologies. You will collaborate closely with a diverse and highly skilled group of scientists and software engineers within our team, as well as with multiple partner teams across the company. Together, we are committed to delivering Atlassian's world-class developer AI products that will shape the future of software development. General Responsibilities - Strategic Leadership: Define and execute the long-term AI/ML strategy, ensuring alignment with product and business goals. - Team Building & Talent Development: Recruit, coach, and develop a high-performing team of engineers and scientists, fostering an inclusive and innovative culture. - Stakeholder Management: Partner with cross-functional leadership (product, engineering, data science) to drive product vision and execution. - Performance Management: Oversee team performance, providing coaching, feedback, and career growth opportunities for direct reports. - Technical Leadership & Mentoring: Provide technical guidance and mentorship to engineering teams, ensuring high standards of code quality and technical excellence. Daily Responsibilities - Project Oversight: Oversee the end-to-end lifecycle of key AI/ML initiatives, ensuring timely delivery and high-quality standards. - Resource Allocation: Balance team bandwidth across competing priorities, ensuring optimal resource distribution for high-impact projects. - Process Optimization: Establish and refine engineering processes to improve team velocity, code quality, and deployment standards. - Risk Management: Proactively identify technical and operational risks, implementing mitigation strategies to maintain team momentum. 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: $209,700 - $273,775 Zone B: $188,730 - $246,398 Zone C: $174,051 - $227,233 - Bachelor's or Master's degree (preferably a Computer Science degree or equivalent experience) - 10+ years of related industry experience in the data science domain, including 5+ years of direct people management experience. You should have a proven track record of building, mentoring, and scaling high-performing machine learning teams, with deep experience in organizational strategy, performance management, and developing engineering leaders. - Experience building and scaling machine learning models in business applications using large amounts of data - Ability to communicate and explain data science concepts to diverse audiences, craft a compelling story - Agile development mindset, appreciating the benefit of constant iteration and improvement It's great, but not required, if you have - Experience working in a consumer or B2C space for a SaaS product provider, or the enterprise/B2B space - Experience in developing deep learning-based models and working on LLM-related applications - Excelling in solving ambiguous and complex problems, being able to navigate through uncertain situations, breaking down complex challenges into manageable components and developing innovative solutions. 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 . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.

Role Description The Staff Engineer - ML Operations is responsible for owning significant parts of the machine learning lifecycle that powers Danaher's next-generation AI-driven research. You will be taking models from experimentation to reliable, scalable production. In this highly impactful role, you will: - Own the end-to-end ML lifecycle and deployment: - Experiment tracking, model registry, versioning, lineage, and reproducibility (e.g., MLflow, Weights & Biases, Kubeflow) - Design and operate model serving for batch and low-latency online inference with autoscaling, GPU efficiency, and performance optimization (batching, quantization, caching) - Partner with bioinformatics and computational biology teams to productionize large-scale protein design and structure-prediction experiments: - Turn research workflows into scalable, repeatable, high-throughput pipelines (Airflow, Dagster, Prefect, Nextflow) - Ensure containerized, reproducible execution that serves many concurrent researchers without contention - Implement CI/CD, continuous training, and observability for ML: - Automate the path from model code to validated production through testing, evaluation gates, and safe deployment patterns (blue/green, canary) - Monitor model performance, data/prediction drift, latency, and cost - Implement automated retraining and alerting instrumented via OpenTelemetry/Prometheus/Grafana - Drive GPU and accelerated-compute efficiency: - Scheduling, quota and utilization management, and driver/CUDA image hygiene - Partner with the platform team to maximize value from contended, high-demand compute - Build self-service ML tooling and provide technical leadership: - Develop golden paths that let data scientists and researchers train, track, serve, and monitor models without deep infrastructure expertise - Set MLOps standards and best practices while staying hands-on with architecture and delivery Qualifications - Degree in Computer Science, Engineering, Computational Biology, or a related technical field, or equivalent practical experience - 5+ years of software, ML, or infrastructure engineering experience, including hands-on MLOps and a track record of taking ML models into production at scale - Strong experience with ML lifecycle tooling — experiment tracking, observability/monitoring, model registry, versioning, lineage, and reproducibility (e.g., MLflow, Kubeflow, Weights & Biases) - Strong experience with containerization and orchestration (Docker, Kubernetes) — including scaling GPU workloads — and with a major cloud platform (Azure preferred) and its ML services (e.g., Azure ML), using IaC and CI/CD for ML - Proficiency in Python (and familiarity with Bash) for automation, tooling, and pipeline development Requirements - Ability to travel – up to 10% - Experience supporting computational biology or bioinformatics pipelines, including protein structure prediction or design tools (e.g., AlphaFold, Boltz/BoltzGen, Chai, RFdiffusion, ProteinMPNN) or molecular simulation - Experience operating ML in a regulated environment (GxP, SOX, or HIPAA), including model traceability and audit evidence - Familiarity with LLMOps/agentic frameworks and evaluation tooling (e.g., Langfuse, OpenTelemetry for LLMs) Benefits - Comprehensive package of benefits including paid time off, medical/dental/vision insurance, and 401(k) to eligible employees - Remote work arrangement available for eligible roles - Annual salary range for this role is $180,000 - $220,000 - This job is also eligible for bonus/incentive pay