Top Tech Jobs & Startup Jobs in Austin, TX

Partners with sales teams to support mid-market customers throughout the pre-sales cycle. Conducts discovery, identifies business problems, maps needs to Atlassian products, leads customized value-based demonstrations, guides technical decisions, expands cross-product opportunities, and builds executive relationships. Tracks customer feedback and competitive intelligence, collaborates with product management, and continuously develops product, solution, platform, and sales expertise.

Leads and develops a team of AI Customer Success Architects delivering enterprise Rovo engagements from implementation through production value. Oversees capacity planning, engagement quality, adoption metrics, customer outcomes, and team growth. Partners with sales, customer success, product, and engineering teams to prioritize engagements, improve delivery methods, surface product feedback, and support AI adoption. The role also involves executive customer relationships, internal and external advocacy, regulatory awareness, and up to 15% travel.

Leads a globally distributed SalesTech engineering team responsible for Salesforce, full-stack applications, microservices, and enterprise integrations supporting Atlassian’s go-to-market systems. Owns technical architecture, reliability, CI/CD, data flows, observability, and integration governance across revenue-critical platforms. Partners with product, program management, architecture, and sales operations leaders while managing engineering capacity, hiring, mentorship, and operational support. Requires extensive enterprise software engineering, Salesforce architecture, systems integration, and people leadership experience.

Serve as a pre-sales technical expert for DX, partnering with Account Executives throughout the sales cycle. Lead technical discovery, evaluations, proof-of-concepts, pilots, product demonstrations, and solution design involving APIs and client engineering workflows. Advise prospects on deployment and analytics integrations, translate technical concepts into business value, and relay customer feedback to Product and Engineering teams. Manage technical follow-ups across multiple opportunities.

Lead Atlassian’s TDP SQL engineering team, owning people leadership, platform strategy, roadmap execution, reliability, security, compliance, incident response, technical direction, cross-functional dependencies, infrastructure economics, and operational outcomes. Build a high-performing organization and evolve a scalable managed relational database platform across multiple cloud environments while supporting enterprise requirements such as data residency, encryption, disaster recovery, and tenant isolation.

Lead adoption of Atlassian’s Teamwork Graph platform by developing positioning, messaging, go-to-market strategy, onboarding programs, enablement, and activation initiatives. Translate complex platform, data, and AI concepts for technical and business audiences, collaborate with Product, Engineering, Sales, and Customer Success, measure adoption analytics, optimize engagement programs, and represent the platform externally.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

Manage a book of 45-75 mid-market accounts to drive net new growth and expansion against a $2-4M quota. Lead cross-functional deal teams, build executive relationships, apply MEDDPICC to qualify and close complex multithreaded SaaS deals, negotiate contracts, source and qualify pipeline, provide accurate forecasting, and collaborate with partners and internal teams. Travel occasionally.

Serve as strategic legal counsel to product and engineering teams on AI/ML, data governance, intellectual property, regulatory, and platform issues. Develop scalable legal frameworks and launch checklists for AI product innovation, act as the primary legal partner for product initiatives, collaborate with legal subject-matter experts, and help build the Product Counsel function’s operating model and culture.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.