Senior Technical Project Manager

We are looking for a Senior Technical Project Manager to join our Product Development Organization. You will drive end-to-end execution of strategic projects within a business segment of our Legal Marketing portfolio — brands such as Avvo, FindLaw, Superlawyers, Martindale-Hubbell, or Lawyers.com — where AI agents and tooling absorb the recurring project‑management work that used to require a full‑time coordinator, and your time is reserved for the operational judgment, stakeholder management, and delivery quality that keep projects on track. This is an AI‑first role. You will operate under a “prove AI can’t do it” gating principle: AI is the default solution for recurring project workflows, and people, meetings, and process are added only after we have demonstrated AI cannot do the work. You drive your projects with AI tooling at the center — using it actively, surfacing structured feedback on what is working and what is not, and owning the quality bar for what your project puts in front of stakeholders. You will be a strong self‑starter with a bias for action — resourceful when tools or data are incomplete, persistent in clearing roadblocks for your team, and comfortable rolling up your sleeves to make progress when the path isn’t obvious. You bring a track record of leading software development projects using agile methodologies, skill in building relationships across teams, and the judgment that comes from making sound trade‑offs between immediate and long‑term needs. You treat AI as a core part of how your projects run, and you understand that as routine work is absorbed by the AI surface, your time shifts to where seniority actually matters. Primary Job Responsibilities Technical design. Drive design reviews for your project workstreams, using AI agents that surface unstated assumptions in PRDs, propose architecture options against historical incidents, and capture decisions from review sessions; participate in program‑level architecture decisions when your project intersects with broader system design. Planning. Drive project‑level planning, dependency identification, and schedule development within the capacity envelope set by the program, using planning agents that produce candidate plans from project requirements, team velocity, and sprint history; bring the judgment to resolve plan‑versus‑capacity trade‑offs the system can’t decide on its own. Scheduling. Own the project schedule, supported by AI surfaces that detect bottlenecks, model re‑sequencing options when dependencies shift, and surface schedule risk; surface cross‑team dependency needs to the program level and own the trade‑offs that stay within the project. Trade‑off decisions. Make scope, sequencing, and resourcing trade‑offs within the project envelope when the AI surface presents options that require judgment on team capacity, timeline, and quality; elevate trade‑offs that go beyond project scope to the program manager. Delivery. Lead project delivery — task planning, scope coverage, dependency resolution, issue management, and launch quality — supported by agents that draft tickets with acceptance criteria, scan in‑flight tickets for scope drift, monitor dependencies, generate launch‑readiness scoring, and analyze integration test coverage; negotiate priorities with partner teams, elevate when needed, and own the launch quality call for your project. Communication. Drive stakeholder communication on project progress, health, risks, and KPI movement (traffic, CVV scores, performance metrics) on cadence and on demand, using AI‑powered status synthesizers, KPI monitors, and risk surfacers; contribute inputs to program‑level executive communication. Knowledge management. Capture, organize, and contribute project decisions, system context, and feature documentation to the program‑level knowledge pipeline, leveraging auto‑extraction that draws from tickets, PRs, design docs, and meeting transcripts; review and validate what the pipeline captures from your project. AI tooling feedback. Use the AI tooling actively within your project — surface structured feedback on what is working and what is not, propose specific improvements based on real‑world usage, and partner with the program manager and engineering on what gets prioritized. Eval operation. Operate the evals defined for the agents in your program, monitor agent health within your project, and flag drift or quality issues for redesign; pull agents back to human review when their outputs fall below the acceptable bar for your project. Practice contribution. Apply consistent project management practices defined for the TPM function — use the established templates, playbooks, and best practices — and contribute back what works and what doesn’t in real project conditions. AI adoption. Champion adoption of AI tooling within your project team — coach engineers and partners on when and how to use the AI surface, surface adoption blockers to the program manager and engineering, and close the loop on team feedback. Skills and Qualifications 4–5 years of technical project management at a technology company, including 2+ years leading software development with schedules and deadlines, preferably in SaaS. Practical experience using LLM‑based agents or AI workflows in production team workflows. Experience using evaluations for AI systems (accuracy, faithfulness, drift detection) to monitor agent health and inform improvement decisions. Technical depth sufficient to read engineering design proposals, understand prompt and tooling decisions, and follow technical architecture discussions. Strong bias for action — proven ability to make progress in ambiguous environments with incomplete information or imperfect tools. Proven track record of leading software development projects across cross‑functional teams using agile methodologies. Experience with standard project planning, road‑mapping, and issue tracking tools. BS/BA degree required, preferably in computer science or technical disciplines. Preferred Qualifications Experience using AI agents in production team workflows in partnership with engineering, including under an AI‑first gating principle. Experience with structured knowledge extraction or retrieval‑augmented systems. Experience driving adoption of new tooling or process within engineering teams. Scrum Master, Scaled Agilist, or PMP Certification. Experience working with cross‑functional partners across product, engineering, and design. Experience owning project delivery end‑to‑end, including communicating status and risk to senior stakeholders. Benefits Health insurance options such as medical, dental, and vision coverage, flexible spending accounts for medical and dependent care, short‑term and long‑term disability insurance, and life and AD&D insurance. 401(k) retirement savings plan with a company match. Paid time off and paid holidays. Commuter benefits and access to an Employee Assistance Program and well‑being coaching services. Voluntary benefits such as home, auto and pet insurance, and discounted legal and financial services. Internet Brands is an equal opportunity employer, and we welcome applicants from all backgrounds. We comply with all applicable laws and regulations, including the City of Los Angeles Fair Chance Initiative for Hiring Ordinance (FCIHO). 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