AI-First Engineering Lead (ClipCrop)
Remote - Hungary
Why us?
🇭🇺 Up to HUF 32,750,000 per year on a full time, contractor contract
🌎 Fully remote working anywhere in Hungary!
✨ Exciting high growth product, relied on by leading global sports brands
💻 Working with the latest hardware, tech stack and tools
About ClipCrop
ClipCrop sits alongside our core product, Storyteller. It helps content teams turn long-form video into high-quality, on-brand clips for different channels – automatically cropping, reframing and templating for each platform so they can publish much more, much faster.
It’s AI-first and automation-heavy: you’ll be shaping how we detect key moments, generate clip variants, and route them into the right workflows and destinations – and how we roll that out to our existing customer base.
This isn’t a “shut the door and code in isolation” role, but you will be hands-on in the codebase. You’ll lead on the technical side of ClipCrop – setting patterns, tackling the hardest pieces, pairing, and reviewing – and you’ll use AI heavily to do it.
Most of your impact will come from:
Framing problems and experiments
Designing workflows and systems (people + AI + code)
Leading the team through AI-assisted implementation and getting changes safely into production
If your ideal day is working alone perfecting hand-written code and you’re not excited about using AI, pairing, and reviews to move the whole system forward, this probably won’t be a good fit.
About us
Storyteller is a high growth B2B SaaS platform, which allows companies to integrate Stories into their owned and operated platforms. Popularized by Instagram and Snapchat, Stories are perfectly suited for boosting user engagement, audience retention, and driving advertising revenue.
Our end‑to‑end platform gives companies a best‑in‑class Stories experience in days with native iOS, Android, and Web SDKs, publishing tools, analytics, and ad support.
We work with many globally recognised clients, particularly within sport, so if you're a sporting fan this could be a great fit!
Responsibilities
Own outcomes, not just tickets
Lead a squad of 2–4 engineers (mix of senior/junior) to deliver measurable product impact in ClipCrop.
Take messy, high-level goals (e.g. “increase good clips published per week”, “reduce time from ingest to published clip”) and turn them into clear bets, experiments, and success metrics.
Break work into small, reversible slices and ship on a steady cadence, rather than running long, risky projects.
Design systems, not just services
Map out workflows and systems (people + AI + code) that achieve the goal, then decide what actually needs to be built.
Decide when to use AI, when to automate, and when to keep a human in the loop – and evolve that over time as we learn.
Ensure the team has the right services/APIs, data models and integrations in place – sometimes by writing code yourself, often by guiding others and joining in at the sharp end when needed.
When you do write code, it’s typically in modern .NET with SQL Server on our multi-tenant SaaS platform, focusing on correctness, observability and reliability more than “perfect” architecture.
Design for reliable operation
Define what “healthy” means for ClipCrop (SLOs, key metrics, dashboards) and ensure the team can see and fix issues quickly.
Instrument services with logs/metrics/traces and hold the line on p95/p99 performance and availability where it matters.
Run incident reviews that produce systemic fixes and better documentation (runbooks, playbooks, dashboards) rather than one-off heroics.
Prioritise for the business
Partner with Product, Design and Customer teams to translate goals into scope; cut or resequence work when data changes.
Make trade-offs explicit: when to ship a simple thing now vs. invest in a more robust approach, based on impact and risk.
Communicate clearly with stakeholders about priorities, progress and changes – no surprises.
Grow people and the codebase
Mentor engineers via pairing, reviews and 1:1s; create growth plans and celebrate momentum.
Keep standards high where it matters: tests in the right places, clear interfaces and intentional technical debt management.
Help the team improve how they reason about systems, not just individual functions or tickets.
Design AI-first workflows
Use AI tools by default for exploration, scaffolding, documentation and analysis – then verify with tests, metrics and your own judgement.
Design processes where AI does the first 80% (e.g. clip suggestion, cropping, captions) and humans review, steer and handle edge cases.
Help the team adopt safe practices (no secrets, anonymised data, prompt discipline, simple evals) and evolve those over time.
You’re willing to delete your own code if an AI-generated or simpler approach gets us to the outcome faster.
Hire and onboard
Contribute to interviews and rubrics; help us identify people who think in systems and love solving problems with AI, not just writing code.
Onboard new teammates with clear goals, docs, and a first-week win that builds confidence and context.
Qualifications
Must‑haves
Technical leadership: you’ve led a small team (2–4 engineers) or a stream of work end-to-end, setting direction, unblocking work, and owning outcomes rather than just tasks.
End-to-end problem ownership: you can talk through at least one example where you took a fuzzy problem from “we’re not even sure what to build” through discovery, experiments, build, launch, iteration and scaling.
Practical problem solving: you start from constraints and business goals, not from technology for its own sake. You cut scope intelligently, make trade-offs explicit, and choose the smallest reversible step that works.
Clear, direct communication: you set crisp expectations with your team and stakeholders, explain decisions in simple language, and give kind-but-candid feedback that actually changes behaviour.
Ownership & accountability: you finish hard things, leave systems healthier than you found them (tests, monitoring, docs), and make accountability routine rather than personal.
Flexibility & product sense: you can change direction quickly based on new information without losing momentum, and you’re comfortable saying “we should stop doing this” as well as “we should do this next”.
AI-native mindset: your instinct is to ask “how can AI and automation do most of this work?” and then design the system, not “I’ll build everything from scratch myself”. You’re comfortable using AI tools and just as comfortable verifying and correcting them.
You don’t need to have “Tech Lead” in your title today, but you should already be acting as the person who joins the dots, makes decisions and drives things over the line.
Nice to have
Cloud & infra: experience with Azure (or an equivalent cloud), CI/CD, feature flags, safe rollouts/rollbacks.
Experience with media/video pipelines, image or video processing at scale, or content/analytics products.
Experience in any modern backend stack and relational database; we use modern .NET and SQL Server, but we’re happy to hire smart people from other stacks who can learn quickly.
Interest in sport and fan engagement (helpful, not required).
We value capability and trajectory over checklists. If you’re strong on most of the must-haves and excited about the role, we’d like to hear from you.
Is this You?
This role will likely suit you if:
You get energy from messy problems and figuring out how to solve them, not from polishing code in isolation.
You like designing systems, workflows and experiments, then using code + AI + people to make them real.
You’re happy that what you do day-to-day will shift over time as tools and products evolve.
It probably isn’t right for you if:
Your favourite days are spent writing code end-to-end yourself, and you’re reluctant to hand work to AI or other engineers.
You mainly want to perfect architecture or critique AI-generated code, rather than decide what the system should do and how we’ll know if it’s working.
Recruitment Process
Step 1 - Intro with the hiring manager (30 min)
A focused conversation about the role, Storypilot, and your experience leading small teams and solving messy problems. No coding.
By the end of this call we’re aiming for a clear “yes, let’s go deeper” or “no, not the right fit” for both of us.
Step 2 - Paid take home
A small task in our stack plus two short written prompts about how you approached it and how you’d evolve it. AI tools are welcome; we care how you reason, how you verify outputs, and how you trade off options.
Step 3 - Review + pairing + interview (75-90 min)
We walk through your submission, pair to extend it, and discuss your past work, decision-making and leadership. We use anchored rubrics and share clear feedback.
How we evaluate
We focus on your ability to structure problems, make sound decisions quickly, and use AI + code as tools to deliver outcomes. We’re not optimising for people who want to spend all day writing code; we’re optimising for problem-solving engineers.