Thunder Compute (YC S24) | C++ Systems, Infrastructure, BizOps | San Francisco (Onsite) | Full-time | https://www.thundercompute.com

We're building the VMware for GPUs. Most GPUs in the cloud sit idle a lot of the time because they're bolted to one machine over PCIe. We decouple them.

Our virtualization layer is a userspace shim, loaded through LD_PRELOAD, that intercepts CUDA calls and ships them over the network to a host with a physical GPU somewhere else in the data center. Workloads run unchanged. When a process goes idle, the GPU detaches and gets reassigned, and reattaches in tens of milliseconds when it's needed again. Think Ceph for GPUs: the GPU becomes a network resource a cluster-wide scheduler can pool and pack.

The hard part is making that fast. We're within ~10% of native on most AI workloads, and it lets us serve ~1.8x more users on the same fleet. We run it in production as our own GPU cloud and sell it to enterprises with underutilized fleets.

Team is from Citadel Securities, Aquatic, Old Mission, AWS, and Bain. Series A, $17.5M raised from Matrix and Y Combinator.

Open roles:

- Software Engineer, C++ Systems ($215K-$325K + meaningful equity). Low-level, latency-sensitive work on the virtualization layer. Quant dev / HFT background preferred: https://jobs.ashbyhq.com/thundercompute/2efae53b-817c-43e7-9...

- Software Engineer, Infrastructure ($150K-$250K + meaningful equity). Go, Kubernetes, reliable systems at scale: https://jobs.ashbyhq.com/thundercompute/927a922b-c607-4b0e-a...

- Business Operations Lead ($150K-$225K + meaningful equity): https://jobs.ashbyhq.com/thundercompute/4f28e418-b77b-446e-a...

If you're cracked at building systems software and want to do something more meaningful than creating liquidity, reach out.

Email me at carl@thundercompute.com and mention HN. I read every email.

Location: Dorset, UK

Remote: Yes (UK, US and EU time zones)

Willing to relocate: No

Technologies: Ruby on Rails, Python (FastAPI), TypeScript/Node, Go, PostgreSQL, AWS, Hotwire

Resume/CV: https://wagnermatos.co.uk

Email: wm@wagnermatos.co.uk

Senior engineer, over 15 years. I build complete products: one senior engineer accountable end to end, AI accelerated. Recent work includes a clinical alerting platform used by NHS trusts, an edge to cloud analytics platform for Johnson & Johnson Medical, and the cloud platform behind a fielded detection hardware fleet. Also open to contract engagements. Case studies and client testimonials on the site.

Snout https://snout.com/ | Multiple Engineering + Product Roles | Remote US or Ontario, Canada | Full Time

Join us at Snout on our mission to ensure no one ever has to make a health decision for their pet based on the cash in their bank account. Snout plans pay for 100% of routine veterinary care, unlimited visits, and additional member benefits - think pet insurance, that you will actually use every year.

We're growing the team and hiring across the board. Featured role this month:

Founding Data Engineer - be our first data hire: make sense of hundreds of millions of rows from vet practice-management systems and our own app, and turn our Postgres and dbt warehouse into a foundation the whole company trusts.

Our tech stack includes Node.js, React, PostgreSQL, AWS, Tailwind, and Python. We use both JavaScript and TypeScript heavily. Our team uses LLM-based tooling day to day, but we're fundamentally a human-centric team, and our product itself does not heavily feature AI components.

Apply at https://jobs.gem.com/snout

PAGNOS | Cloud Engineer, AWS Serverless (TypeScript/Node.js) | REMOTE (Germany) | Full-time | €75k–80k | business-fluent German required

We build serverless applications on AWS for clients and for our own SaaS products. On client projects, we work directly with their developers and help them understand and maintain what we build.

You’ll write backend code in TypeScript/Node.js, build APIs with Lambda and API Gateway, model data in DynamoDB, and design event-driven workflows and integrations. Infrastructure lives in code using AWS CDK, with CI/CD pipelines for builds and deployments. You’ll help decide how systems are designed and take testing, observability and security into account as you build. There’s some React/Next.js work too.

We’re looking for practical experience building AWS serverless applications with TypeScript/Node.js, including infrastructure as code, CI/CD and event-driven design. The role includes working through requirements and technical decisions with clients, so you should be comfortable with that.

We use coding agents daily and expect you to use them too, and to share useful approaches with other developers. AWS serverless development is the main focus of the role.

We’re remote-first, use Scrum, and work with code reviews, pair programming, and unit and integration tests. We meet in person regularly, sometimes in Cologne, sometimes in Austria or Mallorca. We plan travel around family commitments.

You must be based in Germany and speak business-fluent German and good English. We don’t offer visa sponsorship.

€75k–80k, 32 days of vacation, hardware of your choice, and company-paid AI tool subscriptions.

Send your CV to recruiting@pagnos.com – no cover letter needed. Questions before applying are welcome.

Full job ad (German): https://germantechjobs.de/jobs/PAGNOS-GmbH-Cloud-AI-Engineer...

Location: Pisa, Italy (CET) Remote: Yes, remote only Willing to relocate: No Technologies: Python, LLM/agent orchestration, local embeddings, retrieval evaluation, AWS (Lambda/SQS/EventBridge), Terraform, Django, PostgreSQL, LightGBM/PyTorch, NLP/Transformers Résumé/CV: linkedin.com/in/vslovik Code: github.com/vslovik/fenix — local-embedding search whose relevance is actually measured: labelled control probes, blinded human ranking, precision@k. No API keys. Email: valeriya.slovikovskaya@gmail.com

Software architect, 15+ years in production systems, almost entirely startups and internal startups — fintech, e-commerce, pharma, publishing.

The work I get pulled into is the recurring startup problem: a service shipped fast under launch pressure, without adequate tests, that later has to be made reliable without being stopped. Incident response, re-architecture, and the release discipline that keeps it from happening again. Most recently that has meant a regulated UK consumer-credit platform — loan servicing, arrears, forbearance, statutory breathing space, and early-settlement calculations written against consumer-credit legislation. Regulation as code, behind a test suite larger than the production codebase.

I've done that in all three configurations: taking a core system from problem statement to release, leading the team that carried it (1 to 7 engineers in ten months), and now doing the same work again with agentic tooling covering what the team used to.

On the data side: a LightGBM acquisition model over a 38M-row base — 0.77 test AUC, 8x lift in the top 1% — scoring 2.9M households for a live campaign. The part I'd rather be judged on is what happened next: I found a validation-set defect in my own pipeline (early stopping on the test split), quantified its effect across every figure I had already reported, restated them, and added a pure-noise regression test that pins the model to chance when fed random features — so that class of leak cannot come back quietly. NLP is hands-on rather than API-deep: my degree thesis fine-tuned BERT, RoBERTa and XLNet to state of the art on the FNC-1 stance-detection benchmark, published at LREC 2020.

Building on my own time: github.com/vslovik/fenix — it ranks an incoming stream against a free-text description of what you're looking for, and answers questions over the same corpus with citations back to source chunks. Ollama embeddings, sqlite-vec, no API keys. The part worth looking at is the evaluation: the ranking anchor is scored against a labelled probe set with a deliberate control group of things I don't want, and live results are rated blind — scores hidden, order shuffled — so the human judgement stays independent of the ranking it is judging. Doing that produced a measured finding I did not expect: an embedding has no notion of negation, so naming a technology in order to reject it moves the anchor toward it. Numbers and method in lessons/embedding-anchors.md.

Also a tool-calling agent that turns unstructured regulatory text into a deterministic calculation pipeline — the model does the extraction, a deterministic engine does the arithmetic.

Looking for agentic AI/LLM engineering, LLM evaluation and observability, AI integration, or software architecture. Founding-engineer shape suits me — early employee, not co-founder, but early enough to be in the room where the work gets defined. Direct with the company that owns the product: not consultancy, not agency placement, not a body on someone else's engagement.

PrairieLearn (Remote US) — Full-Stack Software Engineer — TypeScript / Postgres / React / AI

PrairieLearn (https://www.prairielearn.com) is an open-source assessment platform used by universities across the US (Berkeley, Princeton, Michigan, Illinois, and others). We power mastery-based learning and large-scale exams.

We’re a small, profitable, early-stage company (bootstrapped, no VC) that is growing quickly. Our users love us and we have very high retention and rapid spread through word of mouth. As an early-stage hire, you’ll work across the stack and enjoy meaningful ownership from day one.

Tech we use: Node.js / TypeScript backend, Postgres, AWS, React. We’re also developing AI tooling, including LLM agents to help instructors create content, and vision-language models to help grade student work. PrairieLearn is open core: https://github.com/PrairieLearn/PrairieLearn

Details:

- Location: Remote (US only)

- Salary: $100k-$180k depending on experience

- Benefits: Stock options (0.5% - 1.5%), unlimited PTO, flexible hours

- Role: Full-time. (We’re not able to sponsor visas at this time.)

Apply at https://www.prairielearn.com/jobs-ashby?utm_source=YeEJ5MAKk...