Principal Technical Program Manager
About The Position
Atlassian is seeking a Principal Technical Program Manager to drive technical strategy, infrastructure planning, and cross-functional execution across its LLM inference platform. The role leads end-to-end programs involving GPU capacity planning, inference optimization, model migrations, and cost-per-token efficiency. It also establishes standardized benchmarking frameworks and governance policies for evaluating, selecting, and safely scaling open-weight models across the organization. The scope will evolve as the platform and industry mature, requiring strong technical judgment, structured execution, and comfort operating in ambiguity. Atlassians may work from an office, from home, or a combination of the two. Atlassian can hire in any country where it has a legal entity.
Requirements
- 10+ years on software teams as a Development Manager or TPM, including 3+ years on ML, AI platform, or large-scale infrastructure programs.
- Working knowledge of LLM architecture, inference optimization, serving frameworks, and GPU infrastructure economics.
- Experience with multi-vendor GPU or inference-provider strategies and AI cost management.
- Experience with benchmarking and model evaluation methods.
- Ability to understand system flows, block architectures, and APIs; TPMs are not expected to write or read code.
- A track record of running end-to-end, complex, cross-organizational technical programs with significant budgets.
- Strategic thinking and ability to translate business objectives into technical programs with measurable results.
- Strong leadership, organizational, and communication skills, including the ability to influence senior engineers and executives without direct authority.
Responsibilities
- Set a long-term strategy and roadmap for the open-weight LLM inference platform, based on business goals, customer needs, adoption barriers, and industry trends, in collaboration with Engineering, Product, Security, and Finance.
- Translate business goals into ranked technical problems, including cost per token, latency, throughput, reliability, and model coverage.
- Define and run programs for GPU capacity planning, inference optimization, model migrations, high availability, and performance.
- Build a consistent approach to benchmarking and evaluating open-weight models for serving performance, quality, and cost.
- Provide leadership and teams with data-backed recommendations for model selection.
- Work with Security, Legal, and Compliance on policies and security requirements for safe adoption of open-weight and OSS models.
- Partner with and influence engineers, ML engineers, and architects to resolve trade-offs and keep programs moving.
- Own programs end to end, from initiation through forecasting, budgeting, and scheduling to delivery, including inference cost models.
- Manage complex dependencies across the company and with external partners, such as cloud and GPU providers.
Benefits
- The role may be eligible for benefits, bonuses, commissions, and equity.
- Health and wellbeing resources.
- Paid volunteer days.
- Additional perks and benefits intended to support employees, their families, and engagement with local communities.
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What This Job Offers
Job Type
Full-time
Career Level
Principal
Education Level
No Education Listed
