Working at Atlassian Atlassians can choose where they work - whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company. As a Principal Machine Learning engineer, you will drive the development and implementation of the cutting edge machine learning algorithms, training sophisticated models, collaborating with product, engineering, and analytics teams, to build the AI functionalities into each Atlassian products and services. Your daily responsibilities will encompass a broad spectrum of tasks such as designing system and model architectures, conducting rigorous experimentation and model evaluations, and providing guidance to emerging ML engineers. Your role is pivotal, stretching beyond these tasks, ensuring AI's transformative potential is realized across our offerings. On the first day, we'll expect you to have - 10+ years of total experience, 5+ years of related industry experience in the MLE / data science domain - Fluency in Python - Solid understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, deep learning, and NLP. - Familiarity with popular ML libraries like sci-kit-learn, Keras/TensorFlow/PyTorch, numpy, pandas - Good Understanding of Machine Learning project lifecycle - Experience in architecting and implementing high-performance RESTful microservices ( API development for ML Models ) - Familiarity with MLOps and experience with scaling and deploying Machine Learning models - Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of "having something now" vs "perfection sometime in the future" - Agile development mindset, appreciating the benefit of constant iteration and improvement It's Great, But Not Required, If You Have - 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. - Experience or passion on building cutting edge developer tools products with AI/ML 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. This role may also be eligible for benefits, bonuses, commissions, and equity. 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.

• Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads. • Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging. • Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time. • Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency and cost visibility, and operational confidence for heavy models. • Work with model owners and platform teams to land pragmatic fixes while helping the team gradually standardize repeated solutions. • Contribute to the team’s technical direction by surfacing patterns, tradeoffs, and opportunities for reuse or automation. • Mentor less-experienced engineers through code, debugging, measurement rigor, and strong execution habits.

• Design, develop, and implement machine learning models and algorithms to optimize operations and service delivery. • Collaborate closely with cross-functional teams to define requirements, conduct feasibility analysis, and deliver scalable solutions. • Lead the full lifecycle of machine learning projects, from ideation through to deployment and maintenance. • Ensure adherence to best practices in machine learning, including model evaluation, validation, and monitoring. • Drive innovation and continuous improvement in machine learning applications and processes. • Provide technical guidance and mentorship to junior team members. • Stay abreast of advancements in machine learning techniques and technologies to recommend improvements and new approaches.

Role Description The Senior Machine Learning Engineer is responsible for developing and implementing machine learning solutions that enhance clinical decision-making, patient outcomes, and operational efficiency across Evergreen's telehealth platform. This role combines deep technical expertise with healthcare domain knowledge to design, deploy, and optimize production-ready AI systems that are accurate, explainable, and compliant with regulatory requirements. Working closely with Engineering, Clinical Leadership, and Product stakeholders, the Senior Machine Learning Engineer will help translate complex clinical and business challenges into scalable machine learning solutions. The ideal candidate is a hands-on engineer who thrives in a fast-paced startup environment, exercises sound technical judgment, and is passionate about building AI capabilities that deliver measurable value while maintaining the highest standards of patient safety and quality. Note on Scope: The Senior Machine Learning Engineer serves as the technical owner of Evergreen's machine learning and predictive analytics capabilities. This role is responsible for designing, deploying, monitoring, and continuously improving the models that power clinical decision support, patient risk stratification, adherence forecasting, dosage optimization, and agent-driven workflows. Working closely with Engineering, Clinical Leadership, and Product stakeholders, the Senior Machine Learning Engineer ensures that AI systems are scalable, explainable, compliant, and aligned with patient safety requirements. The role influences machine learning architecture, model governance, and technical standards while serving as a key contributor to Evergreen's long-term AI strategy. Responsibilities - Design, training, validation, and monitoring of the core predictive and state-transition models powering the clinical product. - Production ML pipelines and retraining cadence, including drift detection and rollback. - Model explainability and validation work needed to support clinical and regulatory review. - Build the evaluation and validation framework for all agent-driven clinical recommendations, including safety guardrails, confidence thresholds, and human-in-the-loop escalation triggers for the Clinical Protocol Agent. - Develop patient risk stratification models for adherence prediction, adverse event likelihood, dosage titration optimization, and churn/dropout risk using clinical, behavioral, and engagement signals. - Design predictive models that support clinical decision-making while maintaining explainability and regulatory compliance. - Collaborate with clinical stakeholders to translate protocols, treatment pathways, and pharmacy domain expertise into model features, training labels, and validation criteria. - Implement and manage the predictive analytics pipeline on AWS, including Amazon Forecast (DeepAR+) for time-series clinical predictions, S3 Vectors for embedding-based patient similarity and retrieval, and Bedrock for agent inference. - Design and build the RAG architecture that grounds agent responses in clinical protocols, formulary data, and operational knowledge sources. - Contribute to machine learning architecture decisions and establish technical standards that support scalability, reliability, and maintainability. - Partner with Engineering leadership to evaluate emerging AI technologies and recommend solutions aligned with business objectives and patient safety requirements. - Own model lifecycle management, including training pipelines, feature stores, model versioning, A/B testing, drift detection, and retraining triggers in production. - Establish model monitoring and alerting, including prediction quality dashboards, distribution shift detection, and automated alerts when performance degrades below established thresholds. - Build explainability layers for clinical recommendations to support provider trust, auditability, and regulatory requirements. - Ensure machine learning systems meet governance, compliance, and documentation standards appropriate for healthcare environments. - Serve as a senior technical resource for AI and machine learning initiatives across the organization. - Provide technical guidance and mentorship to engineers contributing to machine learning, AI, and analytics initiatives. - Partner with Clinical Leadership, Engineering, Product, and Operations teams to ensure models deliver measurable business and patient outcomes. - Work with DevOps/AgentOps resources to ensure all ML decisions are logged, reproducible, and auditable for regulatory review. Qualifications - MS or PhD in a quantitative discipline: Math, Econometrics, Physics, Computational Engineering, or a related field. - Excellent Python skills, with production experience in a modern ML framework such as PyTorch or TensorFlow. - Experience developing and deploying production ML models, not just research or notebooks. - Experience building statistical or neural network models for prediction, classification, or state-transition problems. - Experience with MLOps: model versioning, automated retraining, drift detection, and production monitoring. Preferred Qualifications - Experience developing ML models in a healthcare, clinical, biotech, or pharma context. - Experience with structured clinical data, including FHIR, claims, or EHR data. - Experience building explainable models for regulated or safety-sensitive domains. - Published research, patents, or an advanced thesis involving applied statistical or computational modeling. Key Performance Metrics - Model Performance Accuracy: Achievement of target accuracy, precision, recall, and calibration metrics across production models. - Delivery & Deployment: Successful delivery and deployment of machine learning initiatives against roadmap commitments. - AI System Uptime & Reliability: Availability and operational performance of machine learning services and inference pipelines. - Model Reliability: System uptime, performance monitoring, and timely resolution of model drift or degradation. - Model Drift Detection & Resolution: Timely identification and remediation of model performance degradation. Requirements - Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. - Evergreen Telehealth is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.