Senior Full Stack Engineer

About UsAt ClearJet, we're solving logistics problems for the modern company. By introducing technology into antiquated processes and systems, we drive extreme efficiencies that reduce waste and cost while delighting our customers.As a team, we use GenAI daily to turbo-charge how we work — not as a novelty, but as the default. Our engineers ship with AI-assisted tooling; our analysts query data conversationally; and a growing share of our delivery pipeline is executed by AI agents that pick up well-specified work and run it end to end. Our philosophy is simple: GenAI is a tool that, used correctly, produces outsized results. The teams embracing this wave are positioning themselves for success in the new world.About the RoleWe're looking for Senior Full Stack Engineers to build the system that moves freight — from the database schema to the screen an operator stares at all day.This is an AI-first engineering role, and we're specific about what that means. You will not be writing most of your code by hand, and we don't think that makes the job smaller — it makes it different. The scarce skills are now judgement and verification: decomposing a problem so an agent can execute it, reviewing a diff you didn't type as strictly as one you did, knowing which failures are worth debugging yourself, and holding a design in your head that survives being built quickly. Volume of output has stopped being the constraint. Being right is the constraint.You'll own features end to end: the API, the data model, the UI, the tests, the deploy, and the pager when it misbehaves. There is no throwing work over a wall here — no separate front-end team, no ops team who deploys for you, and no QA who catches what you didn't think about. The QA function owns the release gate; you own leaving work behind that passes it.The domain is unusually satisfying to build for. Freight is real, physical, and constantly wrong: flights slip, carriers miss cutoffs, tracking APIs lie or go silent, and an edge case is a shipment sitting on a dock. The systems you build are used all day by people who will tell you within minutes when you've made their job harder — and thank you when you've made it easier.Our stack: Python/Django + DRF on Postgres and Redis, a TypeScript/Next.js customer-facing app, Docker, Terraform, GitLab CI, Datadog, and a set of carrier, airline, and tracking integrations of wildly varying quality. We're pragmatic rather than religious about tools, but we'd rather go deep on a boring stack than wide on a fashionable one.What You'll OwnFeatures, end to end: Schema, API, front end, tests, release, and operation. You'll pick up a spec and be accountable for the thing working in production, not for your part of it compiling.Backend engineering: Well-modelled Django/DRF services on Postgres — sensible data models, migrations that are safe to run on live data, queries that stay fast as volume grows, and background work that's idempotent and observable.Front-end engineering: Interfaces in TypeScript and Next.js that are quick, accessible, and shaped around how operators actually work. Our users are power users; density and keyboard speed usually beat polish.Third-party integrations: Carrier, airline, and tracking APIs — built defensively, with retries, timeouts, idempotency and clear failure modes, because they will be unavailable, slow, or subtly wrong and that cannot become an outage.AI-accelerated delivery: Use coding agents and LLM tooling as your default working mode — planning, implementation, refactors, migrations, tests, and review. Part of the job is improving how we do this: better specs, better prompts and context, better guardrails, and honest assessments of where agents are earning their keep and where they're producing expensive noise.Review and verification: Review your teammates' work and your agents' work with the same rigour. AI-authored code that nobody genuinely understood is technical debt that arrives faster than the old kind.Quality you don't outsource: Ship with tests. Leave the release gate green. A feature that lands with no coverage isn't finished.Production ownership: Instrument what you ship, watch it after release, and debug it when it breaks. You'll be on a light rotation with the rest of the team.Technical judgement, written down: Make architectural decisions at the right altitude for a small company, and record the reasoning — including what you rejected. Both the humans and the agents that follow you will build from those documents.What Success Looks LikeFirst 30 days: Environment up, codebase understood well enough to be dangerous, and meaningful features shipped to production. You know who the users are and what they complain about.First 60 days: You're taking substantial work from ambiguous intent to live with minimal supervision, and your AI workflow is visibly faster than what you arrived with. Your reviews are improving other people's code.First 90 days: You own an area of the product. You're making design calls others rely on, your work reaches production without drama, and you've improved something about how the team builds — a pattern, a tool, a piece of the agent pipeline — beyond your own tickets.Who You AreA strong engineer who's genuinely comfortable across the stack. You may lean one way, but you don't stop at an interface boundary. You'll debug a slow query in the morning and a rendering bug in the afternoon.Genuinely AI-fluent. You use coding agents daily on real work and can talk specifically about how you've changed your workflow, what you've automated, where you've been burned, and how you verify what a model hands you. Enthusiasm without hands-on usage isn't what we're after.A verifier, not a passenger. You never ship a diff you can't explain. You test the claim rather than the reasoning, you check against the real system rather than your own assumptions, and you're honest when something is unverified.Product-minded. You ask who this is for and what problem it solves, you push back on requirements that don't make sense, and you'd rather ship the smaller thing that helps this week.Pragmatic about engineering. You know the difference between the shortcut you can pay off later and the one that becomes a two-year tax, and you're deliberate about which you're taking.An excellent written communicator. In a distributed, async, AI-first team your writing is your interface — to colleagues and to the agents that build from your specs. Clear thinking on the page is close behind hands-on skill in this role.Independent and async-native. Comfortable working across time zones without supervision, proactive about pulling context rather than waiting to be briefed, and reliable about the commitments you make.Output-oriented. Focused on delivering results within timelines we agree on, and on hitting them without micromanagement.Low-ego and hands-on. You'll write the migration, fix the flaky test, chase the support ticket, and update the doc. Nothing is beneath the role.Balanced. You work hard during the day and value your personal time, stepping in outside hours only for genuine urgency.What We're Looking For5+ years building and operating production web applications, with real depth on both server and client.Strong Python — ideally Django and DRF — with sound relational data modelling and the ability to diagnose a performance problem rather than guess at it.Strong TypeScript with a modern React framework — Next.js preferred — and a working grasp of accessibility and front-end performance.Solid SQL and Postgres experience, including migrations against live data.Comfort owning your own deployments: Docker, CI/CD (GitLab CI is ours), infrastructure as code such as Terraform, and cloud infrastructure. You don't need to be an SRE; you do need to be self-sufficient.Demonstrated daily use of AI coding tools on production work, with concrete examples.Experience integrating third-party APIs that you did not control and could not trust.Testing as a habit rather than an afterthought, and comfort working in a codebase where a red gate stops the release.Excellent written English.Nice to have: logistics, supply-chain, or marketplace domain experience; event-driven or queue-based systems; observability tooling (we use Datadog); building LLM-backed product features; or startup experience where you were one of very few engineers.If you meet most of this and are strong on engineering depth and AI fluency, we'd rather see your application than not.Why Join UsBuild software for an industry that's barely been touched by software, where a good abstraction removes real cost from the physical world.Genuine scope: small team, short decision paths, no committee. You'll make architectural decisions and see them in production the same week.Work in an environment where AI tooling is genuinely embraced, not merely tolerated — you'll get better at it fast, alongside people doing the same, and you'll help define what an AI-first engineering team actually looks like.Close to the users. You'll talk to the operators who use what you build and see the effect within days.A flexible, async-friendly, globally distributed team that respects your personal time and values results over hours clocked.ClearJet is an equal opportunity employer. We hire on the strength of your work and your thinking, and we welcome applicants of every background, identity, and path into this profession.We'd love to hear from you!