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career

4 items tagged with "career"

Event

Mock Interviews for AI Engineering Roles

We will run two mock hiring-manager interviews for volunteer participants, focused on AI engineering roles. The format is useful for most software engineering interviews too. Each interview will roughly follow this structure: - 5 minutes: introduction - project deep dive - about 20 minutes: coding task - 5 minutes: candidate questions After the interviews, we will discuss what happened, what worked well, and how to prepare for this interview style. Volunteer preparation: - Prepare a 2-3 minute self-introduction. - Select 2-3 past projects and review them before the session. - Be ready to explain the problem each project solved, your role and contribution, key technical and product decisions, technologies used, tradeoffs, and why you made those choices. - For more senior roles, be ready to connect the project to business metrics and explain how success was measured. We will invite two volunteers to participate live.

Jul 21, 2026
mock-interviews ai-engineering career
Event

Selecting a Portfolio Project: How to Choose What to Build

Picking a portfolio project is where most people stall. There are too many options, and the default move is to grab a technology you want to learn and bolt a demo onto it. That produces a buzzword demo, not a project a hiring manager takes seriously. In this session you learn a framework for choosing what to build. You start from a real domain, look at real companies, and read what they actually work on. From there you extract the problems they solve, find the ones several companies share, and turn one of those into a project. Technology comes last, chosen to match the stack the companies already use. flowchart LR domain["pick a domain<br/>e.g. education"] -->|shortlist| companies["5-10 companies<br/>in the domain"] companies -->|read| sources["job descriptions<br/>+ tech blogs"] sources -->|extract| problems["problems<br/>they solve"] problems -->|cluster| themes["problems shared<br/>across companies"] themes -->|pick one| project["project candidate"] project -->|match their stack| tech["technology<br/>choices"] We run the framework live in this session. We pick education as a domain and use a coding agent to research the Berlin edtech companies that hire AI engineers. Then we read their job descriptions and engineering blogs, extract the problems they work on, and cluster those into shared themes. From one theme we design a project with a technology stack that mirrors what those companies use. Real data drives every step. The job descriptions come from a monthly scrape of AI engineering roles across several cities, so the findings are grounded in real postings, not guesses. We aim for a project that speaks to several companies in a domain at the same time. That way one piece of work counts for several interviews. Links Resources referenced in the session: AI engineering field guide - the repo where the framework and project-selection process live. The session fills out its portfolio section live. Solving a real AI engineer take-home assignment live - the related session on turning interview assignments into portfolio projects. Personal brand for developers - the companion session on presenting your work once you have built it.

Jun 29, 2026
portfolio career job-search project-selection
Event

Personal Brand for Developers: A 30-Day LinkedIn Challenge

Most of what you do as a developer stays private. You learn a tool, fix a bug, or choose one library over another. Then you move on, and the work disappears. In this workshop you turn that work into public proof of your learning. You take on a 30-day challenge to make posting a habit. The session is about personal brand for developers, focused on LinkedIn. It's not a coding workshop. You'll get comfortable publishing what you learn before it feels perfect, and you'll get a repeatable system, so you never face a blank page. The challenge is small: three LinkedIn posts a week for four weeks. You run the system on a weekly loop. You capture small notes from your daily work as they happen. Once a week you book a short block and pick three notes. You draft each one with an AI assistant, then edit it into your own voice and schedule it. flowchart LR work["daily work:<br/>learning, building, debugging"] -->|capture notes| backlog["idea backlog"] backlog -->|once a week, pick 3| draft["draft with AI"] draft -->|edit yourself| post["publishable post"] post -->|schedule| linkedin["LinkedIn<br/>3 posts/week"] linkedin -->|reflect| backlog Capture and drafting never happen at the same time, and that separation is deliberate. Capture is high-frequency and low-effort. It can be a voice note after a debugging session. It can be a screenshot of an error or a one-line idea saved to yourself. Drafting is a single focused block where you turn raw notes into posts in batches. We spend most of our time getting past the reasons people don't post. You don't need to be an expert. Nothing has to be original. Early posts that get no reactions are normal, and still useful. The rest covers the practical side: where post ideas come from what a good post looks like templates you can copy a live walkthrough of turning a project write-up into a post Links Resources referenced in the session: Steal Like an Artist and Show Your Work by Austin Kleon. Short books on why nothing is fully original and why sharing your process is worth it. The challenge runs in the #linkedin-challenge channel in the AI Shipping Labs community. It has a shared draft-review document and individual draft threads for feedback.

Apr 28, 2026
personal-brand linkedin writing career