Posted by whoishiring 1 day ago
Ask HN: Who is hiring? (March 2026)
Please only post if you personally are part of the hiring company—no recruiting firms or job boards. One post per company. If it isn't a household name, explain what your company does.
Please only post if you are actively filling a position and are committed to replying to applicants.
Commenters: please don't reply to job posts to complain about something. It's off topic here.
Readers: please only email if you are personally interested in the job.
Searchers: try http://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com, or this (unofficial) Chrome extension: https://chromewebstore.google.com/detail/hn-hiring-pro/mpfal....
Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=47219667
At akeno, we help chemical manufactures optimise the production planning in their giant factories. Think asset utilisation, giant dependency graphs, adapting to real-time data, critical processes, a highly complex domain, and scenarios where even squeezing out 1% of extra utilisation can easily save our customers millions.
We're currently looking for 1x Senior/Staff Fullstack Engineer (Typescript, PostgreSQL, React, GraphQL/Hasura, TailwindCSS, Docker), 1x Senior/Staff DevOps Engineer (Kubernetes, Terraform, Observability, multi-environment management, …), and 1x Senior Applied Research Engineer (Python, ML/AI, optimisation) to extend our teams.
You'll need to bring real world experience from designing and running systems that really matter, a true "full stack" mentality (from UX deep down into the DB), team-spirit, and have worked in the EU before. We'll provide: Colleagues who are not only highly competent but you'll actually enjoy working with, 3-2 onsite/home-office model, great office in the heart of beautiful Hamburg, latest Macbook, excellent e2e/integration testing setup, pseudomised customer DB dumps for realistic development data, completely cloud-agnostic, "airplane mode" (you can spin up a full dev environment locally and without internet!), Open Telemetry, and more.
For more info: https://www.akeno.ai/careers and https://www.akeno.ai/tech-radar
Tables power every clinical trial, financial model, and scientific experiment, but deep learning has mostly ignored them. No natural sequence, no spatial structure, no shared vocabulary across datasets. LLM architectures don't transfer. We built TabPFN, the first foundation model that actually understands tabular data (published in Nature, 3M+ downloads, new SOTA for tabular ML). The hardest problems are still open.
The model is half the product. The other half - training infrastructure, real-time serving, developer platform, reliability - is what turns a research breakthrough into something enterprises trust in production. We're hiring across both.
ML Engineer, Cloud Platform — Design and scale the core infrastructure for serving and finetuning foundation models in production. Early enough that you're making the architecture decisions, not inheriting them.
ML Engineer, Training Infrastructure — Own GPU infrastructure, distributed training performance, and the developer productivity layer (CI, experiment tracking, model registry) that keeps research moving fast.
Full Stack Engineer, ML Platform — Build the product that puts tabular foundation models in users' hands, from data upload through inference and results. You'll work across frontend, backend, and directly with the research team to turn new model capabilities into production features.
Research Engineer, Foundation Model — Design experiments, run ablations, build training infrastructure, contribute to papers. Research engineers here aren't supporting scientists — they are the science team.
Also hiring: Research Scientist, Applied Scientist, Forward Deployed ML Engineer, Developer Relations Engineer, AE, BDR.
20-person team selected from thousands applicants. Backgrounds from Jane Street, Google, CERN, G-Research. Led by Frank Hutter, advised by Yann LeCun and Bernhard Schölkopf. With backing from Balderton, XTX Ventures and leaders at Hugging Face, DeepMind, and Black Forest Labs. Comp competitive with top AI labs, meaningful equity.
Apply at: https://jobs.priorlabs.ai
We're a fast growing profitable SaaS that helps stores sell Pokemon cards (and other tcgs). https://storepass.co.
Tech stack: Node.js, Express.js, React, MongoDB
Apply here: https://storepass.co/jobs
Our mission is to empower field-service companies to grow by modernizing and automating their business operations. Every company we work with has unrealized potentials — our task is to build the platform that empowers growth and helps them unlock opportunities.
Details: https://bit.ly/4rQvYzu
Email: jobs [at] 40grid.com (no recruiters or agencies, please put HN in subject line, thanks).
We are applying AI to organize corporate events.
We are looking for a senior engineer with at least ten years of full stack experience, a strong focus on product and UX, and optionally some experience building LLM based products. Currently we are focussed on building tooling that enables us to capture domain knowledge from experts to streamline prompt creation and evaluation. Our tech stack is Typescript, React, Next.js and Tailwind. On the backend we use Drizzle with Neon, deployed and monitored via Vercel. Please note that we are working in the CET timezone. We have pre-seed funding from a major US VC and are experienced founders. You’ll be working alongside the founders (ex-Apple, ex-Airbnb, ex-Tulip, ex-Tourlane) and will have a chance to shape the company, product and of course the engineering culture.
Apply here: https://eventfirst.jobs.personio.com/job/1646275
Details and apply: https://www.docker.com/career-openings/
https://bit.ly/careers-veeva-us OR https://careers.veeva.com/job/8fe22df0-02b4-453d-919c-c8998c...
Transfyr is building physical AI for science.
Why is it that a professional athlete has dramatically more information about every play they make than a scientist has about the cause of any experimental failure? At Transfyr, we are building the infrastructure to make real-world scientific work legible, transferable, and reproducible.
Modern science is capable of extraordinary outcomes, but much of the most important insights never become explicit: how experiments are actually executed, protocols drift, how experts make gametime decisions on the fly, why experiments fail on Tuesdays. This tacit knowledge is rarely captured, making it difficult to reliably reproduce results, much less hand off protocols to new team members or collaborators. We believe our systematic failure to capture tacit knowledge is holding back the entire industry.
We’re building systems that operate directly in real laboratory environments to elucidate, capture, and interpret this missing information. Our platform records and analyzes multimodal data about how scientific work is performed and turns it into durable, operational knowledge. In doing so, we are also building the world’s largest commercial dataset on real-world scientific execution.
This foundation is critical not only for driving elite human performance today, but for enabling meaningful automation tomorrow. Physical AI systems cannot learn from outcomes alone; they require rich, grounded records of how work is actually done in the real world.
Key domains of expertise we’re looking for (hiring from entry level to senior leadership):
- Computer Vision: take messy real-world images and video and make sense of them
- AI / ML Research & Engineering: build the best digital brains to support the best physical brains on the planet
- Full Stack Engineering: integrate our AI and data into tools that solve real customer problems.
A change from when I posted here previously: we are now focused on on-site in-person roles, at our lab/office in Cambridge, MA. And, we are particularly interested in DevOps/backend experience for the full stack role.
If you're interested, apply here! https://transfyr.ai/join-us
Hey! I’m Maxim, cofounder/CEO of Immunera.
We're building blood tests that use gene sequencing and machine learning to help patients with autoimmune disease. Immunera spun out of years of research at Stanford, and our technology has been published in Science and covered by The New York Times.
We're hiring a Principal Machine Learning Engineer to shape the core models and infrastructure that power our blood tests. We are partnering with hospitals to generate training data from real patients for our language models and other biological sequence models.
This is a senior engineering role with significant autonomy at an early-stage, venture-backed startup. We're looking for strong experience building and operating ML systems in production (Python, PyTorch/TensorFlow, cloud platforms). Prior biology or healthcare experience is NOT required; we care much more about your ability to reason about data, models, and systems.
Full JD: https://www.immunera.ai/jobs
Contact: maxim@immunera.ai (subject: “HN ML job”)