We're building an AI-first bioinformatics platform to empower life science researchers with agentic AI and LLMs. Backed by pre-seed funding, a government grant, and customers from biotech, agrotech, and pharma companies, we're looking for a full-stack engineer to join as a core member.

What we're looking for:

• Strong full-stack experience across Go, TypeScript, and Python (Rust is a plus)

• Hands-on AWS infrastructure work (you've built and maintained production cloud environments)

• Experience with multi-tenant architectures: isolation strategies, auth, data partitioning

• Comfort with AI/ML devops: deploying and orchestrating model-serving pipelines, managing GPU workloads, CI/CD for ML systems

• Willingness to own problems end-to-end, from system design to shipping

Nice-to-haves (not all required): LLM integration, RAG systems, HPC infra, cloud/on-prem hybrid systems

About us: We're a team of engineers and scientists with strong academic and industry backgrounds (ex-BCG, PhD in Plant Biotech and Bioinformatics, Mathematics, M.Sc. AI, PhD in Electrical Engineering). We're committed to building something impactful, technical, and genuinely useful for life science.

Apply: hello@ploid.ai

More about us: https://ploid.ai

We also consider internship requests from strong student candidates for research work.

Thanks to everyone who's reached out through previous HN posts, this community has helped us find amazing people.

I built a small Cursor extension that tracks LLM cache timers so you can see when your context cache is about to expire.

While working at Ploid AI (ploid.ai), I noticed that most cache rewrites, both mine and my teammates', came down to two things: forgetting to continue the right chat, or forgetting to review plans before the cache dropped off. Basically, money and context lost to simple timing mistakes.

So I built this extension to give that extra information. It won't magically make you a better coder or promise cost savings, but it gives you a bit more visibility into what's happening under the hood.

GitHub: https://github.com/agastalver/cache-timer-extension

Extension: https://open-vsx.org/extension/agastalver/cache-timer-extens...

Would love feedback or suggestions.

Comments URL: https://news.ycombinator.com/item?id=47632209

Points: 2

# Comments: 0

We’re building an AI-first bioinformatics platform to empower life science researchers with agentic AI and LLMs. Backed by pre-seed funding, a government grant, and real demand from biotech and seed companies, we’re looking for versatile engineers to help us shape the future of AI in genomics.

What you’ll do:

• Architect and build AI workflows, agentic interfaces, and data pipelines

• Work across the stack: Go, Python, TypeScript

• Contribute to system design, infrastructure (AWS, Docker, Terraform), and devops

• Collaborate closely with scientists and iterate from prototype to product

• Help define our technical culture and roadmap as a core founding member

Nice-to-haves (not all required): LLM integration, RAG systems, HPC infra, ML, cloud/on-prem hybrid systems

About us: We’re an early team of engineers and scientists with strong academic and industry backgrounds (ex-BCG, PhD in Plant Biotech, M.Sc. AI). We’re committed to building something impactful, technical, and genuinely useful for life science.

Apply: hello@ploid.ai

More about us: https://ploid.ai

We also consider internship requests from strong student candidates.

IMPORTANT: We are now seeking for expertise in frontend / full-stack and PhDs with AI applied to life science bioinformatics (multiomics and/or chem)

Thanks everyone for all the support in our August and September posts! HN really helped finding amazing engineers.

We’re building an AI-first bioinformatics platform to empower plant breeders and life science researchers with agentic AI and LLMs. Backed by pre-seed funding and real demand from biotech and seed companies, we’re looking for versatile founding engineers to help us shape the future of AI in genomics.

What you’ll do:

• Architect and build AI workflows, agentic interfaces, and data pipelines

• Work across the stack: Go, Python, TypeScript

• Contribute to system design, infrastructure (AWS, Docker, Terraform), and devops

• Collaborate closely with scientists and iterate from prototype to product

• Help define our technical culture and roadmap as a core founding member

Nice-to-haves (not all required): LLM integration, RAG systems, gRPC, scientific computing, cloud/on-prem hybrid systems

About us: We’re an early team of engineers and scientists with strong academic and industry backgrounds (ex-BCG, PhD in Plant Biotech, M.Sc. AI). We’re committed to building something impactful, technical, and genuinely useful for life science.

Apply: hello@ploid.ai

More about us: https://ploid.ai

We also consider internship requests from strong student candidates.

Update to our August post: Thank you for all the interest from HN! We have filled 2 out of the 3 open positions. We are now seeking for expertise in full-stack development.

We’re building an AI-first bioinformatics platform to empower plant breeders and life science researchers with agentic AI and LLMs. Backed by pre-seed funding and real demand from biotech and seed companies, we’re looking for versatile founding engineers to help us shape the future of AI in genomics.

What you’ll do:

• Architect and build AI workflows, agentic interfaces, and data pipelines

• Work across the stack: Go, Python, TypeScript

• Contribute to system design, infrastructure (AWS, Docker, Terraform), and devops

• Collaborate closely with scientists and iterate from prototype to product

• Help define our technical culture and roadmap as a core founding member

Nice-to-haves (not all required): LLM integration, RAG systems, gRPC, scientific computing, cloud/on-prem hybrid systems

About us: We’re an early team of engineers and scientists with strong academic and industry backgrounds (ex-BCG, PhD in Plant Biotech, M.Sc. AI). We’re committed to building something impactful, technical, and genuinely useful for life science.

Apply: hello@ploid.ai

More about us: https://ploid.ai

We also consider internship requests from strong student candidates.

This is a great point, in fact is the main purpose of the model. The model is validated against genomes with variations which were never seen to check its prediction capabilities.

AFAIK, variants are created either by changing/adding/shuffling already known genes. And not trying to create an unknown mutation of a gene we never saw before. Therefore, our thesis is that with enough information we can capture the effect of the gene in the final product.

Of course, there is epigenetics as well; which is tackled with additional information about the environment.

It seems like selecting the correct variants have a great impact in time-to-market and variant acceptance rate as cultivation takes years to perform.

I was wondering whether this (or something similar) has been done before and what were the challenges you might have encounter.

Feel free to ask for more information :)

Comments URL: https://news.ycombinator.com/item?id=39300402

Points: 1

# Comments: 2