Senior Technical Program Manager Job ID 2026-1580

Internet Brands•El Segundo, CA

1d•$100,000 - $199,999

About The Position

We are looking for a Senior Technical Program Manager to join our Product Development Organization. You will lead large-scale, cross-functional initiatives across our Legal Marketing brands — Avvo, FindLaw, Superlawyers, Martindale-Hubbell, Lawyers.com — where AI agents and tooling absorb the recurring program-management work that used to require entire program teams, and your time is reserved for the strategic judgment, executive communication, and cross-functional trust-building that AI cannot replace. 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 TPM workflows, and people, meetings, and processes are added only after we have demonstrated AI cannot do the work. You drive your programs with AI tooling at the center — using it, shaping it in partnership with engineering, and owning the quality bar for what your program 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 medium to large projects using agile methodologies, deep skill in building relationships across an organization, 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 programs run, and you understand that as routine work is absorbed by the AI surface, your time shifts to where seniority actually matters.

Requirements

7+ years of technical project management at a technology company, including 3+ years leading software development with schedules and deadlines, preferably in SaaS
Practical experience using LLM-based agents or AI workflows in production, and contributing to their design, prompts, and evaluation
Experience contributing to evaluations for AI systems (accuracy, faithfulness, drift detection) and using them to drive quality decisions
Technical depth sufficient to read engineering design proposals, debate prompt and tooling strategies, and contribute to AI-based solution evals
Strong bias for action — proven ability to make progress in ambiguous environments with incomplete information or imperfect tools
Proven track record of leading complex programs across cross-functional teams using agile methodologies
Experience with standard project planning, roadmapping, and issue tracking tools
BS/BA degree required, preferably in computer science or technical disciplines

Nice To Haves

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 across engineering teams
Scrum Master, Scaled Agilist, or PMP Certification
Experience owning program strategy, end-to-end delivery, and communicating results to senior leadership
Experience in a company at scale or previous work in a large software development organization

Responsibilities

Technical design. Lead solutioning and technical review for your programs, using AI agents that surface unstated assumptions in PRDs, propose architecture options against historical incidents, and generate ADRs from review sessions; partner with engineering on what these agents need to do next.
Planning. Drive capacity and resource planning, dependency identification, and project schedule development, using planning agents that produce candidate capacity models and schedules from product 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 cross-team scheduling for your programs, supported by AI surfaces that detect bottlenecks, model re-sequencing options when dependencies shift, and surface schedule risk; set the policies these surfaces operate under for your program and own the trade-offs when the call becomes strategic.
Trade-off decisions. Make scope, sequencing, and resourcing trade-offs when the AI surface presents options that require judgment on business impact, organizational context, executive priorities, or partner commitments the system can’t see.
Delivery. Lead program delivery — task planning, scope coverage, cross-team dependency resolution, issue management, and launch quality — supported by agents that draft tickets with acceptance criteria, scan in-flight tickets for scope drift, monitor cross-team dependencies and contract drift, generate launch-readiness scoring, and analyze integration test coverage against critical paths; negotiate priorities, break ties, escalate to leadership, and own the launch quality call.
Communication. Drive stakeholder communication on project progress, health, risk, and KPI movement on cadence and on demand, using AI-powered status synthesizers, KPI monitors, and risk surfacers; own executive communication on strategic initiatives where the narrative matters as much as the numbers.
Knowledge management. Lead knowledge management for your program — capture, organize, and distribute feature and system decisions and documentation, leveraging auto-extraction pipelines that draw from tickets, PRs, design docs, and meeting transcripts; help define the schema, evals, and refresh cadence for what your program contributes.
AI tooling contribution. Actively shape the AI tooling roadmap for your program — propose what to automate next, partner with engineering on design and prioritization, and surface real-world feedback that drives the tooling’s evolution.
Eval design. Design and operate evals for the agents in your program — accuracy, faithfulness, drift over time — and pull agents back to human review when evals fall below threshold. Eval design for your program is yours, not an engineering hand-off.
Practice leadership. Drive consistent, high-quality project management practices across the TPM function — develop and maintain templates, playbooks, and best practices that capture how AI-augmented program work gets done at scale, and evolve them as the AI surface matures.
AI adoption. Lead adoption of AI tooling within your program teams — coach engineers and partners on when and how to use the AI surface, surface adoption blockers back to engineering, and close the loop on team feedback so the tooling actually gets used.