AI-First Engineering Lead (Storypilot) at Friedman

, , -

Full Time

Start Date

Immediate

Expiry Date

05 Mar, 26

Salary

166000.0

Posted On

06 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Technical Leadership, End-To-End Problem Ownership, Practical Problem Solving, Clear Communication, Ownership & Accountability, Flexibility, AI-Native Mindset, Cloud & Infra, Media/SaaS Experience, Backend Stack Experience, Interest In Sport, Mentoring, Designing Systems, AI Tools Usage, Hiring, Onboarding

Industry

Media Production

Description

🇧🇬 Up to BGN 166,000 per year on a full time, contractor contract 🌎 Fully remote working anywhere in Bulgaria! ✨ Exciting high growth product, relied on by leading global sports brands 💻 Working with the latest hardware, tech stack and tools ABOUT US Storyteller is a fast-growing B2B SaaS platform that lets companies add Instagram-style Stories (short form video) to their own apps and sites - boosting engagement, retention, and revenue. Our iOS, Android, and Web SDKs deliver a complete Stories experience with publishing, analytics, and ad tools. We’re now building an AI-native, multi-product platform that helps partners create and personalise immersive content. We work with leading global brands, predominantly in sport - so if you love technology, are always looking to learn, develop and harness the latest technology, and have an interest in sport - you’ll fit right in. About Storypilot Storypilot is our content intelligence platform for sports and media. It ingests data and content from a wide range of sources – social media, match data, internal systems and more – and uses AI to surface insights, generate ideas and content, and help customers decide what to publish next. It’s AI-first and AI-enabled by design: you’ll be shaping how AI turns messy, multi-source data into workflows, suggestions, reports and new content – 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 Storypilot – 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. RESPONSIBILITIES We think you’ll be a great fit if you’ve worked in fast-moving startup/high-growth environments where autonomy, adaptability, and ownership are part of the culture. Own outcomes, not just tickets Lead a squad of 2–4 engineers (mix of senior/junior) to deliver measurable product impact in Storypilot. Take messy, high-level goals (e.g. “help editors find better stories faster”, “increase high-quality content output per week”) 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. Design for reliable operation Define what “healthy” means for Storypilot (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% 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 across media/SaaS, SDKs, data pipelines 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. ---------------------- Privacy Notice We process your personal data for recruitment purposes in line with UK data protection law. AI tools may assist in reviewing applications, but decisions are made by our team. We retain data only as necessary for recruitment and compliance. You can request access or deletion of your data at any time by emailing careers@getstoryteller.com.

Responsibilities

Lead a squad of engineers to deliver measurable product impact in Storypilot, transforming high-level goals into clear experiments and success metrics. Design systems that integrate people, AI, and code, ensuring reliable operation and effective communication with stakeholders.