Teja S.

I CAN’T HIRE SR. DEVS!! If you keep publicly repeating that an intern with an AI tool is equivalent to a senior engineer, do not act surprised when your recruiting pipeline for senior talent collapses. Senior engineers are not applying for a coding job. They are applying for judgment, ownership, and the burden of making hard calls under ambiguity. Your public posture is a signal. It broadcasts how you price experience, how you assign accountability when things break, and how you will treat the people who carry the risk. And it hints at the decisions you will make during interviews, leveling, compensation calibration, and, later, incident reviews when the easy demos fail under real constraints. It is important to remember that hackathons are not real engineering, they are time-boxed improvisations that optimize for a demo, not correctness, maintainability, observability, or long-term operational risk. #AI #Engineering

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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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I've recently taken a 180 w.r.t. agentic coding. It happened when my coworker put up a PR authored by Claude Opus 4.5 which implemented a complicated LLM eval workflow in one shot, and which was ~95% correct. When I leverage coding agents, code review becomes the new time-to-ship bottleneck, instead of code writing. A developer can become an architect, coordinating an army of agentic devs. And its not limited to opening a first draft PR. GitHub MCP supercharges addressing PR comments. Datadog MCP supercharges observability tasks. There are other examples. But a machine can't be held accountable for the decisions it makes. We are accountable for the code LLMs create for us. So its our responsibility to fix, iterate on, and verify what they produce, until we stand by their diffs. This is just the next step for me. Like I said, code review is the *new* bottleneck. Still thinking about how to lift that bottleneck e.g. empirical validation over opaque code via trusted testing and red teaming, etc.

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📊 ML Developers: One Small Resume Detail Quietly Filters You Into the Top 5% 🤖 😓 Most ML resumes look impressive but feel academic. 😮 One small detail makes hiring teams pause instantly. 🧠 It quietly proves your model changed decisions in the real world. 🚨 That detail is tying one model to a business or system outcome after deployment. 🔍 Most resumes list algorithms without proving production impact. Pro Tip: Connect one model to accuracy lift, cost reduction, or decision speed. 📉 “Trained ML models” signals low ownership. Pro Tip: Name the exact prediction and what improved because of it. ⚠️ Metrics listed without context feel empty. Pro Tip: Explain why the metric mattered to the business or product. 📦 Missing deployment signal hides seniority. Pro Tip: Mention inference environment, scale, or monitoring setup. 🧪 No model drift or retraining signal weakens trust. Pro Tip: Add how you detected drift or improved stability over time. 🧩 Feature engineering claims feel generic alone. Pro Tip: Tie one feature change to measurable model improvement. 🛠️ Pipelines mentioned without outcomes get ignored. Pro Tip: Show how automation reduced training time or errors. 📊 Dense explanations bury the signal. Pro Tip: One model, one decision, one result only. ⏱️ Old experiments first reduce relevance. Pro Tip: Lead with your most recent deployed ML system. 📨 Resume shows theory, not consequence. Pro Tip: Always state what became better after predictions went live. 🔥 STOP applying blindly! Follow us for real job-winning strategies. 🚀 Want daily job search hacks that actually get results? 👥 Follow JobLanderz.VIP for daily job hacks & insider tips! 📣 Tag a friend who's job searching! Help them land their dream job faster. #MLDevelopers #MachineLearningJobs #AIEngineering #ATS2026 #JobSearchTips #JobLanderzVIP 🚀 Extra Tip: Top ML resumes connect one deployed model to one measurable decision improvement only.

The job description is officially dead. Anthropic recently shared an internal survey on how their team uses Claude Code. The results were a wake-up call for every builder. Non-technical employees are now debugging. Engineers are using AI to streamline non-core tasks. The walls between "technical" and "creative" are melting. In a world where intelligence is democratized, "staying in your lane" is the fastest way to become irrelevant. This hits Founders the hardest. The days of the "pure" CEO are over. • Non-technical founders --> must now have technical intuition. • Technical founders --> must now have marketing & distribution intuition. Role definitions are merging into one single requirement: Resourcefulness. If you aren't doing things outside of your typical scope, you aren't moving fast enough. STOP saying "that's not my job." START being curious - "is that possible?", "How can I create more impact?"

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