Staff Software Engineer

JLL (Jones Lang LaSalle)

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
San Francisco, CAChicago, IL
Salary
$180,000–$190,000 / yr
Posted
38 days ago
Freshness
Confirmed live yesterday
Closes
Oct 23, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $221k
This role $185k
$166k most similar roles pay here $275k

This role pays less than 80% of similar roles. Most pay $192,510–$250,000 — the shaded band above. At the midpoint, this role pays about $185k versus about $221k for comparable roles.

Based on 240 similar postings.

Employer

About JLL (Jones Lang LaSalle)

JLL (Jones Lang LaSalle) is a global professional services firm specializing in real estate and investment management, providing services to buyers, sellers, tenants, landlords, investors, and developers. Industry: Commercial Real Estate Services

JLL (Jones Lang LaSalle) currently has 75 open roles on FindRole.

Listed pay typically runs $147,000–$190,000 across 35 roles with salary data.

Most-posted roles

View all roles at JLL (Jones Lang LaSalle)

At a glance

TL;DR · Staff Software Engineer

As a Staff Software Engineer on the MarTech Engineering Team, you will serve as a forward-deployed engineer embedded within the marketing organization to build production AI agents. You will work directly with marketing domain experts to translate their workflows into automated solutions for campaign management, content production, and performance analytics. Your daily responsibilities include rapid prototyping, building integrations with enterprise systems like CRMs and CMSs, and hardening pilots into reliable, scalable products. You will utilize modern LLM APIs, prompt engineering, RAG systems involving vector stores, and multi-step reasoning to solve complex marketing problems. The role requires expertise in Python or other relevant languages, as well as experience with cloud environments, observability, and cost monitoring. You will bridge the gap between technical infrastructure and marketing needs to ensure agents provide factually correct, brand-aligned actions across a complex marketing stack.

What does a Software Engineer earn in California?

Median $214000 from 775 postings across 63 companies.

See salary data

What you'll do

  • Build and deploy production-grade AI agents to automate marketing tasks like content creation, campaign management, and performance monitoring.
  • Collaborate directly with marketing experts to translate their workflows into functional, automated agent capabilities.
  • Develop a library of reusable LLM workflows for common tasks such as drafting, scoring, and brand-voice tuning.
  • Build integrations between AI agents and existing marketing technology stacks including CRMs, CMSs, and ad platforms.
  • Manage the full lifecycle of agent development from rapid prototyping to production hardening and reliability monitoring.
  • Identify infrastructure gaps during pilot phases and translate them into technical requirements for the platform engineering team.
  • Ensure all AI outputs remain factually grounded, brand-aligned, and safe across complex marketing systems.
  • Represent the MarTech team by publishing internal reports and external content regarding agent engineering best practices.

What we're looking for

  • You must have a Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent work experience.
  • You must be proficient in English for communication in remote and asynchronous work environments.
  • You require 5+ years of software engineering experience working in production systems and integrating with enterprise APIs.
  • You must have hands-on experience with modern LLM APIs, including prompt engineering, tool use, and structured outputs.
  • You must have experience designing agent systems involving multi-step reasoning, tool orchestration, memory, and error recovery.
  • You must have experience building RAG systems using embeddings, vector stores, and retrieval optimization.
  • You must have experience deploying LLM-powered services to production cloud environments with proper security and monitoring.
  • You must be able to translate complex technical concepts for non-technical stakeholders and collaborate in distributed teams.

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