Xplor Technologies powers the experiences at the heart of everyday life. Through modern vertical software, embedded payments, and AI-powered capabilities, we help businesses in fitness, recreation, golf and club, field services, laundry, education, and other membership-based and service-based industries simplify operations, uncover insights, and elevate customer and member experiences. 

We unite popular brands such as Clubessential, foreUP, myFitApp, Vermont Systems, Momence, Exerp, and many more.

Full-time · Remote · 3–5 years of experience

Why this role exists:

We're hiring a full stack engineer who can move fluently between backend services, frontend UIs, and AWS infrastructure — and who treats the stack as a tool to solve problems, not a tribal identity. We don't care if you're "a Java person" or "a JS person." We care that you can pick up an unfamiliar codebase on Monday and ship something useful by Friday, with AI tools amplifying your throughput rather than slowing you down. If you find yourself bored when a problem requires switching languages or layers, this isn't your team. If that's the part you actually enjoy, keep reading. If your background is specifically in shipping LLM-powered features (RAG, agents, evals), see also our Full Stack Engineer, AI Products role — it might be a closer fit.

What you'll do

You'll own features end-to-end — from the database schema, through the service layer, to the UI the user actually clicks. You won't be handed a narrow vertical; you'll be expected to think across the whole stack and make sensible trade-offs at each layer. Concretely, in your first six months you'd expect to:

• Ship at least one significant feature end-to-end, from data model to deployed UI

• Take ownership of a backend service or domain — its API contract, p95 latency, error budget, and on-call quality

• Move a measurable infra metric (cost, deploy time, error rate, or alert noise) in the right direction

• Help shape how the team uses AI tools across backend and frontend work — prompts, evals, agentic flows

Day-to-day, you'll write code (a lot of it AI-assisted), review PRs, debug across the stack, and partner directly with product, design, and other engineers. There's no buffer layer of EMs or PMs translating things for you.

What we're looking for

We're optimizing for a particular kind of engineer. Specific stacks can be learned in weeks; the things below take years.

A genuine willingness to learn. Not the LinkedIn-bio kind — the kind where you've actually picked up a new language, framework, or domain in the last twelve months because the problem demanded it. We change tools when better ones show up, and we expect you to do the same without complaint.

Adaptability across stacks. You shouldn't have a tribal identity around any one technology. Java, Node, Python, Go — whatever the problem calls for. The engineers who do well here are the ones who treat "I haven't used that before" as a one-week problem, not a blocker.

Strong fundamentals. Data structures, algorithms, concurrency, networking, caching, transactions, security. Solid enough that you can apply them to unfamiliar problems instead of pattern-matching on frameworks you've used before.

System design at both altitudes. You can sketch a high-level architecture for a multi-service product — service boundaries, data flow, failure modes, scaling pressure points — and you can also do the low-level work: schema design, API contracts, class structure, hot-path optimization. Most candidates are good at one and weak at the other; we need both.

Sharp problem-solving. You break ambiguous problems into tractable pieces, you can hold a complex system in your head, and you debug from first principles rather than guessing.

Hands-on enterprise-scale experience. You've built and operated real services at meaningful scale — not toy projects, not demos. You understand request lifecycles, transactions, queueing, retries, idempotency, and the difference between code that works in dev and code that survives production traffic.

Strong backend foundations on an enterprise-grade framework. Spring Boot, .NET, NestJS, Django, or equivalent.

Solid frontend chops. Real experience in at least one of Angular, React, or Vue — and ideally a working knowledge of a second. You can build a non-trivial UI from a Figma file without handholding, and you have opinions on state management, component design, and performance.

Polyglot comfort. Java and a JavaScript-based backend (Node/Nest/Express) is a strong plus, but the meta-skill matters more than any specific language.

AWS at depth, not surface. You've shipped production workloads on AWS using several of: EC2, ECS, Lambda, Step Functions, DynamoDB, RDS, S3, SQS/SNS, API Gateway, CloudWatch. You can debate when Lambda is the wrong choice, when DynamoDB beats Postgres (and vice versa), and what a Step Functions state machine should and shouldn't be doing.

Both database paradigms. SQL (Postgres or MySQL) and NoSQL (DynamoDB, Mongo, or similar). You can model data in both, you know what each is good at, and you don't reach for one out of habit.

Fluency with AI-assisted development. This is not optional and not "exposure." We expect you to be using Claude Code, Cursor, Copilot, or equivalent every day, and to have real opinions on:

• When to use agentic loops vs. inline completions vs. just writing it yourself

• How to scope a task so an agent can actually finish it

• How to review AI-generated code critically (the failure modes are different from human code)

• How to build internal tooling, scripts, and evals around AI workflows

Tell us in your application what your current setup looks like and what you've built with it.

High autonomy. You're comfortable with ambiguous specs, async communication, and making calls without a committee. You raise your hand when blocked, but you rarely are.

Nice to have

• Experience with infrastructure-as-code (Terraform, CDK, Pulumi)

• Event-driven architectures, CDC pipelines, or distributed workflow patterns at scale

• Performance work — query optimization, caching strategies, profiling under load

• Multi-tenant SaaS experience, especially around isolation and data partitioning

• Built developer tools, internal SDKs, or AI-powered dev workflows

• Open-source contributions or a public body of work we can look at

• Prior experience in a small, fast-moving team (under ~30 engineers)

Got questions? You can email us at talentsupport@xplortechnologies.com.