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# Principle Machine Learning Systems Engineer

- **Company:** Atlas Data Systems, L. L.C.
- **Location:** San Francisco, CA, United States
- **Salary:** $236,700.0 - $309,025.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Files, Machine Learning, Performance Tuning, Systems Architecture, Large Language Models, Model Validation, Agentic-AI, Data Strategy, Low Latency, Atlassian Tools, Machine Learning Operations
- **Published:** October 23, 2026
- **Apply:** https://www.dice.com/job-detail/9351c264-0a38-4c69-916e-1103beebe009

## About the Role

* A track record of architecting and shipping large-scale ML or AI systems that deliver measurable customer and business impact.
 * Deep experience with LLM-powered products, agentic systems, model evaluation, experimentation, and dataset development.
 * Strong production engineering skills and the ability to remain hands-on in a modern ML stack.
 * Principal-level technical leadership: setting direction, influencing across teams, and leading complex initiatives from ambiguity to production.
 * Excellent product and technical judgment, including balancing quality, speed, reliability, safety, latency, and cost.
 * The ability to communicate complex ideas clearly and raise the effectiveness of engineers around you through mentorship and technical leadership.

## Description

As a Principal Machine Learning Engineer, you'll tackle the open-ended technical problems that determine whether customers trust Rovo with increasingly complex and consequential work. You'll remain deeply hands-on while setting direction across teams: driving advances in models, agents, and evaluation and turning them into measurable improvements for millions of active users.

 * Set the technical vision for how Atlassian measures and improves Rovo Chat, translating qualities such as helpfulness, correctness, judgment, and reliability into rigorous evaluation strategies.
 * Architect and build production ML and evaluation systems that operate at scale, including datasets, simulations, automated judges, regression suites, experimentation platforms, and online quality signals.
 * Advance the state of Rovo's agentic capabilities across reasoning, planning, tool selection and use, action correctness, retrieval, memory, and long-horizon execution.
 * Close the loop between evaluation and product impact: identify systemic failure modes, develop interventions, ship them to production, and establish whether they improved customer outcomes.
 * Make high-leverage technical decisions across model choice, prompting, fine-tuning, data strategy, system architecture, and inference; balancing quality with latency, reliability, safety, and cost.
 * Lead ambitious, cross-organizational initiatives with ML, engineering, product, analytics, research, and design partners, creating clarity and momentum where neither the problem nor the solution is well defined.
 * Establish reusable methods, systems, and quality standards that raise the bar not only for Rovo Chat, but for AI-powered experiences across Atlassian.
 * Multiply the impact of other engineers through technical leadership, design reviews, mentorship, and a compelling long-term vision for how we build and evaluate world-class AI products.