Renctly Meta and lot's of companies are poaching guys from other companines i want to check what do they know that guys from their own company dont it was experiecena and secrate saurce

Designed for my own by ChatGPT based on my profile

BTW i have a prompt where i keep my qualification and paragraphs about me i just feed it to rate me as per it

I’ll give you the tough love version since you asked for it 🔥 — if you want to be on the radar of big labs (OpenAI, Anthropic, DeepMind, Meta FAIR, CZ Biohub, Arc Institute, Recursion, Inceptive), you need to move from being a “bioinformatics engineer” to being seen as a “frontier genomic AI researcher/engineer.” Right now, your profile is strong but reads as applied academic bioinformatics + solid engineering. That’s not yet poachable at the $500K+ tier. Here’s what you need to do in the next 12 months:

1. Move From User → Builder of Frontier ML

• ✅ You’ve used LLMs with omics data.
• ❌ But frontier labs will ask: what did you contribute to model architecture or training methods that nobody else has?

👉 What to do:

• Learn transformer internals cold: attention math, rotary embeddings, mixture of experts, retrieval augmentation, gating, KV caching, training optimizations (ZeRO, LoRA, FlashAttention, DeepSpeed).
• Implement at least one model from scratch (say, a genomic foundation model like DNABERT-2 or Enformer) and improve it.
• Target: submit a preprint where you improve predictive accuracy on enhancer-promoter interactions / 3D genome folding using transformer innovations.

2. Scale Experience

Big labs poach people who’ve actually trained or deployed at scale:

• Meta cares if you can manage multi-node, multi-GPU jobs with sharded weights, efficient checkpointing, and distributed data pipelines.
• Your AWS experience is good, but running “20 GPUs for a pipeline” ≠ “2,000 GPUs for training.”

👉 What to do:

• Get hands-on with distributed training frameworks: PyTorch FSDP, DeepSpeed, Ray.
• Build a small cluster (could even be simulated with 4 GPUs on LambdaLabs or Paperspace) and run scaling experiments.
• Blog/write about “Scaling genome foundation models: lessons from training Enformer on 1B parameters”. This gets recruiter eyes.

3. Push Into Frontier Bio + AI Fusion

Right now, “Genomic AI” is becoming hot (Nvidia, Recursion, Inceptive, Deep Genomics, Google Isomorphic Labs). The poachable edge comes from new architectures + biological insight.

👉 You should:

• Focus on 3D genome + transformers: use Hi-C and ATAC-seq with attention-based models to predict enhancer-promoter loops.
• Or protein + genome multimodal models: integrate sequence + structure + chromatin.
• Or drug discovery pipeline with LLM reasoning: genome → target → compound → pathway.

This makes you not just another “pipeline bioinformatician” but someone leading the fusion of foundation models and biology.

4. Get Your Work Public

Poached engineers have signals:

• OSS repos with 1k+ stars (e.g., “genomic LLM toolkit”)
• Preprints that hit bioRxiv/Arxiv trending
• Benchmarks that others use

👉 You should:

• Open-source a genome transformer repo with clean, production-ready training code.
• Publish at least one first-author preprint in the next 12 months that ties ML to genomic discovery.
• Write technical blogs on Medium/LinkedIn/Twitter: “Why training genome-scale LLMs is harder than language LLMs”, “Scaling Hi-C analysis with transformers”.

5. Sharpen the Signaling Skills

Your projects are impressive, but recruiters at Anthropic or DeepMind don’t parse “built IDE with shadcn + FastAPI.” They care about:

• Novel ML contributions (did you invent or adapt a new method?)
• Scale (how many parameters? how big the dataset?)
• Impact (does your model predict something nobody else’s does?)

👉 Rewrite your projects like:

• Instead of “LLM integrated with ChIP-seq and Hi-C” → say “Developed a transformer model integrating 1B genomic tokens across ChIP-seq, RNA-seq, and Hi-C, improving enhancer-promoter interaction prediction by 14% over Enformer baseline.”
• Quantify, benchmark, compare. That’s how engineers get poached.

6. Network Like It’s Part of the Job

• Apply to fellowships (OpenAI Superalignment, Schmidt AI in Science, Biohub NYC fellowship).
• Present at workshops (NeurIPS Genomics, ISMB ML track, RECOMB).
• DM engineers on X/LinkedIn who work at Anthropic/DeepMind and share technical posts.

Recruiters follow technical noise → noise creates FOMO → FOMO creates offers.

🎯 TL;DR — One-Year Roadmap

If you want to be poachable:

1. ML Depth → Know transformer guts + distributed training.
2. Scale Cred → Run models at multi-GPU scale.
3. Novel Contribution → Do 1-2 projects that combine new AI architectures + genome data.
4. Public Signal → Preprint, OSS repo, technical blog.
5. Narrative Shift → From “bioinformatics pipeline engineer” → “genomic AI architect.”

Do that, and in 12 months, you’ll have the profile that makes recruiters at DeepMind, Meta, or Inceptive DM you instead of the other way around.

⚡ Do you want me to map out a concrete 12-month learning & project roadmap (month-by-month) so you know exactly what to study/build/publish to hit this “poachable” tier?