Staff Software Engineer

About the role

As a Staff Software Engineer on the MarTech Engineering Team, you'll be a forward-deployed engineer embedded in the marketing organization, building production AI agents that solve real campaign, content, social, ABM, and performance problems. You'll work shoulder-to-shoulder with marketing domain experts (SMEs)—sitting in their workflows, learning their systems, and turning their expertise into agents that go live and stay live.

Your remit spans the full lifecycle: rapid prototyping with SMEs to validate ideas, building the integrations and skills needed to ship a working pilot, and hardening pilots into production-grade agents that operate reliably at scale. When something is missing—a CRM that doesn't expose what you need, a data source not yet in the warehouse, a guardrail that doesn't exist—you'll either build a pragmatic workaround to unblock the pilot or translate the gap into a clear, prioritized requirement for our partner platform team to address. You sit at the seam between domain expertise and platform infrastructure, ensuring neither side waits on the other.

The role demands deep technical judgment: when to build vs. wait, when to abstract a one-off solution into a reusable skill, and how to ensure agent outputs translate into safe, brand-aligned, and factually correct actions across a complex marketing stack. Success is measured by the volume and quality of marketing work that agents are actually doing in production—not by demos or POCs, but by agents that marketers trust to do the job. This is an opportunity to build the agentic layer of one of the world's largest commercial real estate platforms from the ground up and see your work change how marketing operates at global scale.

Who you are

• You have a Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent work experience.
• You are proficient in English, both written and verbal, sufficient for success in a remote and largely asynchronous work environment.
• You have 5+ years of software engineering experience working in production systems, including significant time integrating against enterprise APIs (CRMs, CMSes, DAMs, ad platforms, marketing automation tools, analytics).
• You have hands-on experience with modern LLM APIs across providers—including prompt engineering, tool use/function calling, structured outputs, and context engineering—and understand that the underlying patterns transfer even as APIs evolve.
• You have experience designing agent systems: multi-step reasoning, tool orchestration, memory, error recovery, and human-in-the-loop escalation paths.
• You've worked through the hard parts of agent engineering firsthand—hallucination grounding, tool reliability and silent failures, evals that predict real-world behavior, cost and latency tradeoffs, prompt drift, and the difference between "works in a demo" and "still works on day 30." You can discuss what you've tried, what failed, and what you learned.
• You have experience building RAG systems—embeddings, vector stores, retrieval optimization, and grounding—and a strong intuition for when retrieval is the right solution vs. a tool call, fine-tune, or schema change.
• You've deployed LLM-powered services or agents to production cloud environments and understand operational realities like auth, networking, secrets, observability, rollback, and cost monitoring.
• You're energized by embedding directly with non-technical domain experts, can translate vague problem statements into shippable scope, and have the patience to learn an unfamiliar domain deeply enough to anticipate where agents will fail.
• You have a pragmatic bias for shipping—knowing when a brittle workaround is the right call to unblock a pilot and when something deserves to be built right the first time.
• You translate fluently between technical levels—explaining agent failure modes to non-technical SMEs, briefing leadership on architectural tradeoffs, and diving deep on protocol details with platform engineers, often within the same day.
• You have strong analytical and interpersonal skills, thrive in dynamic, product-focused, distributed teams, and embrace a proactive approach to problem-solving.
• You're confident taking ownership of projects from start to finish and enjoy turning nebulous ideas into reality while making your coworkers feel included in every interaction.

Responsibilities

• Embed with marketing SMEs across campaigns, property marketing, content production, social, ABM, and performance intelligence to design and ship agents that do real work in their day-to-day.
• Build, deploy, and operate production marketing AI agents that reason over regional context, brand guidelines, and intent data—and invoke tools to draft content, configure campaigns, monitor performance, and recommend optimizations.
• Design and grow the Marketing Domain Skills Library—composable LLM workflows (drafting, scoring, classification, brand-voice tuning) extracted from live agent work as reusable primitives.
• Build integrations against marketing systems (CMS, DAM, CRM, marketing automation, ad platforms, analytics)—directly when needed to unblock a pilot, and through Marketing MCP servers built by the platform team once available.
• Translate integration and capability gaps you hit during pilots into clear, prioritized requirements for the platform team, so the platform layer evolves to meet real agent needs.
• Own reliability, observability, evaluation, and cost efficiency of LLM-powered workflows in production—including brand-voice checks, factual grounding against property and client data, regression suites, and offline benchmarks wired into CI/CD.
• Design multi-agent orchestration patterns: how the Campaigns agent coordinates with Social, ABM, Content, Property, and Performance Intelligence; where to compose vs. keep boundaries; and how escalations and handoffs flow.
• Set the bar for the agent pod: define the playbook for going from SME conversation to working pilot to deployed production agent, and raise the technical quality of what the team ships.
• Represent MarTech Engineering externally—to JLL leadership, customers, and the broader engineering community—as a credible voice on building agents that work in production.
• Publish what you learn—internal write-ups, engineering blog posts, and conference talks—to sharpen the team's thinking, raise the hiring bar, and contribute to the broader agent engineering community.
• Stay at the frontier of agent engineering and bring the best ideas back to the team, continuously raising the bar on quality, performance, and architecture at scale.
• Drive innovation with a willingness to experiment and boldly confront problems of immense complexity and scope.