Clara Kim

One trend I’m seeing across AI startups: Founding engineers are becoming more selective than ever. They’re choosing: → Strong technical leadership → Clear roadmap → Real ownership Not just comp. This is shifting hiring dynamics fast.

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6 months ago, Cabal was a search UI. You'd type a company name, browse connectors, click through tabs. It worked. But it wasn't how people actually think about relationships. People think in questions: "Who can intro me to the VP Eng at Datadog?" "Which of my LPs know the Figma founding team?" This is why we built the Cabal agent, now available in-app or on Slack. Open Cabal or type @Cabal in slack, ask a question in natural language, get ranked intro paths in seconds.

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If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG

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The industry doesn't need more people who can "write code." LLMs can do that now. The industry needs people who can: 1. Identify bottlenecks before they happen. 2. Structure systems for 10x growth. 3. Architect AI-native workflows. The Republic Sale ends in 48 hours. 70% OFF HLD + LLD + AI Combos : https://lnkd.in/g5rUq944

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LLMs are everywhere and I believe one of the biggest challenges right now is aligning them to real-world utility Some thoughts from recent work: - Fine-tuning is great, but prompt engineering still wins for many lightweight use cases. - Evaluating with user-facing metrics (not just accuracy or perplexity) changes how you build. - Bias & fairness issues pop up even with apparently clean data. If you’re working with LLMs, what evaluation metrics do you prioritize, and why?

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📚 Hiring Labs — Episode 4: Grads, It’s Tough Out There (But Here’s How to Make It Work) Let’s be real: If you just graduated with an engineering or software degree, this market is not playing nice. 🧊 Entry-level hiring has frozen at some of the biggest tech firms. 📉 Job listings for new grads are down ~50% since 2019. 😓 Nearly 6% of grads are unemployed, and over 40% are underemployed—working in jobs that don’t require their degree. And here's the truth: Graduating into a weak market like this can cause up to a ~9% salary penalty, lasting years. But... taking smart moves now can set you up for serious payoff later. Let’s talk about what recruiters are really noticing—and what might be holding you back. ❌ What’s Not Helping You Right Now Pricing yourself out of the market Asking for $120K in Orlando or Huntsville because your cousin's roommate got that in Palo Alto? Nope. Taking a ~$50–60K job now and learning fast can put you in that $120K seat sooner than you think. Unemployment = lost income and lost momentum. ❌ Relying on your degree alone Everyone applying has the same degree. What else do you bring? ✅ Projects ✅ GitHub repos ✅ Stack Overflow activity ✅ Open-source contributions Even self-initiated work counts. Show you’re hungry. ❌ Ignoring the tools If you haven’t touched: – GitHub – AI tools (yes, including ChatGPT & Copilot) – Python frameworks – Docker or Kubernetes – Cloud platforms (AWS, Azure, GCP) …then you’re not showing recruiters you’re ready right now. ❌ Ghosting recruiters You might be ghosting your way out of your first job. Even a 10-minute chat can plant a seed. You don’t have to say yes—but you do have to show up. And yes, you should vet recruiters too: Do they know tech? Are they active in the space? ✅ What Does Get You Noticed – A clean, tailored resume that connects to the job posting (see Hiring Labs Episode #1!) – Proof of life: GitHub, LinkedIn, a portfolio site—something we can actually see 🎓 This cycle is tough, no doubt. But recession grads who stay visible and adaptable are often the ones leading teams five years later. Because while your degree opens the door—your hustle gets you in. — 💼 Want to know what we’re hiring for right now? Take a look: https://lnkd.in/etJQ5jCE

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🚀 We’re partnering with top software engineers in New York’s early-stage startup scene. Talent is moving fast, but our clients are listening and adapting as engineers face real challenges: Interview fatigue is real. Engineers are burned out from marathon processes, long take-homes, and ghosting feedback. “We don’t get to see the product”. Companies aren’t demoing or explaining the product early on, and it’s a big turn-off. The best candidates want to believe in what they’re building. Show, don’t just tell. Tech rounds are moving up. More companies are front-loading the technical interviews to avoid time wasted later, and it’s working for both sides. Remote is shrinking. We’re seeing a definite tilt back toward onsite or hybrid. Not everyone loves it, but the shift is happening. It’s less about a coding language, more about product. Hiring managers are becoming less rigid about specific stacks, and more focused on alignment with product vision, user impact, and client collaboration. 📩 Reach out for more insights: george.wilson@stottandmay.com #Hiring #SoftwareEngineering #TechRecruiting

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As a Staff Applied Scientist at Datadog, Ben Cohen is working on AI research that is helping to shape the future of observability. After leaving in 2022, Ben returned to Datadog in 2024 to join the AI Research team. “I was thrilled to come back to Datadog to help grow our AI research lab. With the scale, data, and leadership support we have here, we can build things that don’t exist anywhere else. We process data points at an incredible scale and speed, making Datadog the perfect place to work on cutting-edge AI projects,” says Ben. Ben co-led the development of Toto, a state-of-the-art time series foundation model, and BOOM, a large-scale, observability-focused time series forecasting benchmark. He’s now training expert SRE agents using reinforcement learning. As Ben puts it, “This capability will radically change how engineers interact with observability tools.” Want to work on AI that changes the future of observability? https://lnkd.in/esZgXiaU Read more about Toto and BOOM here, and meet the team at NeurIPS 2025 next week: https://lnkd.in/eEnWiAFn #DatadogLife #AI

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