# Senior / Staff Software Engineer (Remote or In-Person) **Company:** [Impruve](null/companies/d7qM1LZkyMUxV8WYB4VVtr.md) **Location:** Remote **Workplace:** remote **Employment type:** Full-time **Department:** Engineering [Apply for this job](null/view/ec8138cc-6aec-4d33-9e97-977c1691d344) ## Description **Senior / Staff Software Engineer, Chicago (remote or in-person)** Impruve is a boutique forward-deployed engineering firm that helps wealth management firms get real business results from AI. We embed directly with our clients to build production data pipelines, AI tools, custom software, and compliance infrastructure for firms managing billions in client assets. We have more demand than we can currently serve, and we're growing the team to meet it. Our founders previously built and exited Packback, an AI company that served more than 3 million users. **WHO WE ARE** We're four engineers right now, and we're building how we work on purpose, not backfilling a culture after the fact. We care about doing right by clients and by each other. We take the work seriously: real client money, real compliance risk, real production systems. We don't take ourselves too seriously. We pair early and often, across the team, because it's how we actually get better. If you notice something interesting mid-task, we want you to flag it and pull someone in, not disappear down a rabbit hole solo. When something breaks, we run blameless postmortems; the point is fixing the system, not finding someone to blame. We don't expect you to walk in with all the answers. Nobody here has them either. We expect you to be scrappy: willing to try something, see what happens, and figure it out with the team rather than wait until you're sure. You won't be joining something fully formed. You'd be joining our founding engineering team, with real say in how this team works, not just what it ships. Ask us about that in the interview and we'll actually tell you, not recite a values poster. **WHY THIS ROLE** Most wealth management firms want to bring AI into their business, and they have access to the frontier models and the tooling around them. What's missing is the engineering capability to deploy AI effectively and in a compliant manner. That's the gap Impruve fills, so wealth managers can spend more of their time improving the lives of their clients. You'd own client engagements end-to-end, from scoping the work with the client through architecting and shipping the system. This role is for engineers who are excited about getting hands-on experience deploying AI in production. **WHAT YOU'LL DO** - Embed directly with client teams and own engagements end-to-end: scoping the problem, shipping the system, and managing the client relationship. You'd typically run multiple client engagements in parallel. - Work AI-natively: you're fluent with agentic development workflows (spec-driven development, custom agent skills, and the frameworks emerging around them) and you continuously refine how AI multiplies your output. - Move across the full stack: data engineering (pipelines, transforms), AI engineering (RAG/graphs, evals, memory, agent workflows), and the backend and frontend work that ties it together so it holds up in production. - Architect platforms that change how clients operate. You're building reliable, observable systems with clean UX that keeps humans in the loop. - Shape how we run engagements and how Impruve scales to become the leading forward-deployed engineering firm in wealth management. You'd be instrumental in building our practice. **WHO THRIVES HERE** You would be a great fit if you identify with these: - 5+ years of hands-on engineering experience, most of it building and operating production systems. - You design and ship reliable systems. You turn ambiguous problems into clean production code. - You move comfortably between data, infrastructure, backend, and AI. You don't need to be an expert in all four, but you can make sound calls and own an engagement without constant escalation. - You've worked directly with external stakeholders through consulting, professional services engineering, forward-deployed engineering, or solutions engineering. - Hands-on AI systems work: agentic workflows, RAG pipelines, LLM integrations, evals, or memory systems, built as the core of the product rather than bolted onto a legacy workflow. - You're excited about AI as a force multiplier for engineering. You actively experiment, form opinions about what's working, and bring new techniques back to the team. - You hold your own in front of clients. You can explain technical concepts without oversimplifying or hiding behind jargon, including the hard conversations about scope and tradeoffs. - You judge your own work by what changed for the client, not by what you produced. Stack: Python, Node.js, Express, TypeScript, Next.js, MongoDB, Redis, AWS ECS, Docker, LangChain/LangGraph. We care more about sound judgment across domains than a checklist match. **HOW WE HIRE** Three steps: an initial conversation, a team interview with the founders that includes a technical problem-solving session, and a final decision. **ONE MORE THING** Research consistently shows that women and people from underrepresented groups are less likely to apply to a role unless they check every box, while others apply anyway. If that's you: **apply anyway**. We care more about curiosity, scrappiness, and kindness than a perfect resume match, and we build a better team when the people on it don't all think alike. Requires US work authorization; we can't sponsor visas. ## Requirements Strong Engineering Fundamentals: You design and ship reliable systems. You turn ambiguous problems into clean production code. 5+ Years of Hands-On Engineering Experience: Most of it building and operating production systems. Cross-Domain Fluency: You move comfortably between data, infrastructure, backend, and AI. You don't need to be an expert in all four, but you can make sound calls and own an engagement without constant escalation. Client-Facing Track Record: You've worked directly with external stakeholders through consulting, professional services engineering, forward-deployed engineering, or solutions engineering. Hands-On AI Systems Work: Agentic workflows, RAG pipelines, LLM integrations, evals, or memory systems, built as the core of the product rather than bolted onto a legacy workflow. AI-Native Mindset: You're excited about AI as a force multiplier for engineering. You actively experiment, form opinions about what's working, and bring new techniques back to the team. Comfortable in the Room: You hold your own in front of clients. You can explain technical concepts without oversimplifying or hiding behind jargon, including the hard conversations about scope and tradeoffs. Bias for Outcomes: You judge your own work by what changed for the client, not by what you produced. Our stack: Node.js, Express, TypeScript, Next.js, MongoDB, Redis, AWS ECS, Docker, and the AI tooling ecosystem around LangChain and LangGraph. We care more about your judgment across domains than a checklist match. This role requires US work authorization. We are not able to sponsor visas.