Senior Technical Project Manager

Relativity•Colorado Springs, MI

7d•$96,000 - $144,000•Remote

About The Position

Relativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy and trust are crucial. The world’s largest law firms, corporations, and government agencies rely on Relativity’s legal AI software to securely surface and manage the most relevant and impactful information in their matters. Beyond our commercial impact, we’re deeply committed to expanding access to technology for academic institutions through Relativity Academic and supporting pro bono legal work through Justice for Change. WHAT WE DO Relativity’s engineering organization is large, distributed, and fast moving, with teams empowered to choose the tools and practices that best fit their domain. Engineering plays a critical role in delivering a secure, scalable cloud-based SaaS platform that supports high-stakes legal work around the world. Infrastructure TPM Team The Infrastructure Technical Project Management function partners closely with engineering and product leaders to drive execution across complex, cross-functional initiatives. This role requires senior-level judgment and independent ownership. You will operate as a strategic delivery leader, not a meeting facilitator or status collector, by anticipating follow-up questions, surfacing what’s missing, and pushing teams from noise to signal. You will help leaders move from status updates to decision support by clarifying outcomes, tradeoffs, dependencies, and risks, and by bringing strong accountability mechanisms that accelerate execution without adding unnecessary overhead. Relativity is also leaning heavily into AI across both our product offerings and our internal engineering practices. This role requires curiosity, experimentation, and practical application of AI-enabled tools to improve delivery quality, increase speed, and reduce manual overhead in planning, reporting, and project execution management.

Requirements

5 to 7 years of experience leading medium to large technical projects or programs in cloud, SaaS, infrastructure, or engineering-focused environments
Advanced proficiency with Jira Cloud, Jira Advanced Roadmaps, Confluence, and Asana
Demonstrated ability to lead complex cross-functional work across multiple engineering teams with differing priorities, tools, and delivery styles
Experience driving alignment, decision-making, and delivery across senior engineers, engineering managers, and cross-functional leaders in a fast-moving environment
Strong technical fluency and systems thinking, with the ability to understand architecture-level concepts, technical dependencies, and engineering tradeoffs
Clear understanding of cloud infrastructure, automation, CI/CD, observability, and modern engineering practices, with the ability to apply that context in planning, dependency management, and delivery decisions.
Proven ability to create clarity in ambiguous situations and translate incomplete information into actionable execution plans
Track record of identifying delivery risks early, driving mitigation, and improving outcomes through proactive intervention
Proven ability to describe delivered work in terms of outcomes, metrics, tradeoffs, and business or engineering impact, not just activities completed
Strong judgment and executive communication skills, including the ability to synthesize complexity into concise, decision-ready updates
Demonstrated ability to influence without authority and hold teams accountable through partnership, credibility, and follow-through
Hands-on experience working with GitHub and related engineering workflow tooling to support planning, execution tracking, issue management, and cross-team coordination.
Practical interest in AI and emerging technologies, including hands-on use of AI-assisted tools to improve delivery outcomes or team workflows
Comfort operating in an organization that is actively increasing investment in AI-driven product capabilities and AI-enabled engineering practices

Nice To Haves

Experience working in Azure, AWS, or GCP-based environments
Experience working with distributed engineering teams across regions, including the ability to drive alignment, accountability, and execution across time zones and differing team norms
Experience supporting engineering efficiency, platform automation, cost optimization, or operational improvement initiatives in technical environments
Experience building or improving TPM operating models, governance, reporting, or portfolio-level execution practices
Experience using AI tools to support planning, communication, meeting synthesis, reporting, risk identification, dependency analysis, or workflow automation
Exposure to AI or machine learning initiatives through direct project support or self-driven learning and experimentation
Experience managing cross-team planning, and operating effectively even when data quality is imperfect
Relevant project or program management certifications such as PMP, PMI-ACP, PMI-RMP, or equivalent Agile credentials

Responsibilities

Lead the end-to-end delivery of complex technical initiatives across engineering domains
Drive alignment on business outcomes, scope, sequencing, risks, dependencies, ownership, and success measures across multiple teams
Translate ambiguous goals into clear execution plans, operating rhythms, decision points, and accountability structures that enable teams to move faster
Operate as a strategic delivery leader by asking the next question, surfacing gaps, and pushing for the rigor needed to execute well
Challenge weak assumptions, plans, timelines, and inputs when needed to improve decision quality and reduce delivery risk
Identify risks early, assess impact, drive mitigation planning, and escalate with recommendations rather than simply reporting problems
Partner closely with engineering leaders and teams to understand technical context, architecture implications, dependencies, and delivery constraints well enough to guide execution credibly
Provide concise, leadership-ready updates focused on progress, risks, changes, decisions needed, and recommended actions
Facilitate discussions that drive teams to closure on priorities, tradeoffs, commitments, and next steps
Improve execution quality by identifying patterns, friction points, and systemic gaps across teams and helping implement better ways of working
Explore, adopt, and help scale AI-enabled tools and practices that improve project management, including planning support, technical synthesis, status reporting, dependency analysis, risk detection, and decision support
Lead or support initiatives tied to AI-enabled product capabilities, AI experimentation, and AI-related engineering investments as priorities evolve
Partner effectively with engineering and product leaders as the organization increasingly incorporates AI into its product strategy, development approach, and internal tooling