Footprint | Rust Engineers (Backend, Infrastructure, Security) | New York, San Francisco | ONSITE | VISA (case by case) | $180K-$260K + equity

We build Percy, an agent that runs financial-crimes investigations for banks and fintechs. It clears false positives, escalates real risk, and writes the case narrative and audit record a regulator will read. Under it sits four years of identity infrastructure: verification, an AWS Nitro Enclave-backed vault for PII, and the APIs our customers call directly. The backend is Rust.

We're hiring three Members of Technical Staff:

- Backend: the Rust APIs and services behind verification, vaulting, and the checks Percy runs. Production Rust is ideal; deep systems experience plus a real wish to learn it also works. Crypto or systems-security background counts for a lot.

- Infrastructure: own agent compute, isolation, deploys and observability across Rust services and long-running agents. Rust, Postgres/Aurora, AWS (ECS/Fargate), Pulumi/Terraform, Datadog, OpenTelemetry.

- Security: protect identity data we vault on behalf of regulated banks and fintechs, and prove to auditors every step was done right.

Small, senior team. Backed by QED, Index and BoxGroup. Customers include FDIC- and OCC-regulated banks plus Bilt, Nuvei and MoonPay.

Apply: https://jobs.ashbyhq.com/footprint. I'm the recruiter and will answer questions in this thread.

Prior Labs | Berlin, Freiburg, NYC | ONSITE | Full-time | Technical PM, ML Infra, Research Scientist/Engineer, Backend, Full Stack

We build foundation models for tabular data. Deep learning transformed text and images but mostly skipped tables, which are still the data behind most clinical trials, financial models and scientific experiments. The reason is structural: no natural sequence, no spatial structure, no shared vocabulary across datasets, so the architectures and scaling laws behind LLMs don't transfer.

Our approach: pre-train a transformer on millions of synthetic datasets sampled from causal-structure priors. The whole dataset goes in as context, predictions come out in one forward pass. No per-dataset training, no hyperparameter tuning, seconds instead of hours. TabPFN v2 was published in Nature and set a new state of the art; TabPFN-3 scales to 10M rows. 4M+ downloads, 8k+ GitHub stars, in production from liquid biopsy to rail maintenance. Code: https://github.com/PriorLabs/TabPFN

Since July we're an independent lab inside SAP, with more than EUR 1B committed over four years. Models stay open, research stays public, same team and offices.

Roles (most can sit in any of the three offices):

- Technical Product Manager, Integrations: take every model release live across SAP (AI Core to SAP Analytics Cloud), the cloud marketplaces and customer environments, and help decide which channels we build next. Reports to me, close to the code.

- ML Engineer, Infrastructure: own multi-cluster GPU infra (Slurm on GCP today, multi-provider next), training performance and the tooling layer. We spend tens of millions per year on compute; you own that budget.

- Research Scientist, Foundation Model: drive the model agenda - novel architectures, scaling from 10K to 1M+ samples, multimodal and causal directions. PhD plus top-venue publications, or equivalent.

- Research Engineer, Foundation Model: same agenda from the engineering side. You design experiments, write the training and eval infra, and co-author the papers.

- ML Engineer, Backend: design and scale the backend that serves and finetunes the models. Python/FastAPI, Terraform, K8s.

- Full Stack Engineer, ML Platform: build the product end to end. TypeScript + Python, React/FastAPI/Postgres.

Also hiring: Applied Scientist, Forward Deployed ML Engineer, Research Scientist (Foundational Data Science), PhD research interns, plus GTM and ops roles.

40+ people with backgrounds from Google, DeepMind, Jane Street, Goldman, G-Research, CERN. Led by Frank Hutter, advised by Bernhard Schölkopf and Yann LeCun. Comp competitive with top AI labs.

All roles and applications: https://jobs.ashbyhq.com/prior-labs

Questions welcome in the replies here.

Hiya | Senior Backend Software Engineer | Seattle, WA | HYBRID (3 days/week in office) | Full-time | $113K–$197K + equity

Hiya makes phone calls trustworthy again. We do caller ID, spam and scam protection, and branded calling for carriers, phone makers, and businesses.

I run the Communications Platform team (Seattle + Barcelona). We're building new systems where telephony meets AI: real-time scam detection on live calls, deepfake voice detection, speaker verification, and AI call screening that plugs into carrier networks. Most of it is new work, so you'll help make early architecture decisions.

Stack: TypeScript/Node.js, SIP/RTP, STT/TTS, Docker, Kubernetes, AWS, Terraform, Helm.

Looking for 3+ years of backend experience, strong TypeScript or Python, and comfort with APIs and event-driven systems. VoIP/telephony experience is a big plus but not required. I came to telecom from a software background too.

Apply: https://jobs.ashbyhq.com/hiya/2de4d41b-a919-48c4-9a6c-e0d4f3...

Radar Labs | Software Engineers (SRE, ML, backend, full-stack, mobile, security, QA) | Remote (US), NYC | Full Time | https://radar.com

- Radar is the geo-location dev tool

- Doing 1B+ API calls per day

- Our main languages are Rust and TypeScript, we also use mobile and offline pipeline languages (Python, Scala, and Terraform).

- We're based in NYC with our HQ in Union Square and remote friendly (US)

Interesting things we're working on:

- HorizonDB, our Geospatial database written in Rust

- Agents and MCP tools to visualize and debug location data at scale

- Precise indoor location more accurate than iOS and Android leveraging Ultra-Wideband, other mobile sensors and ML.

- Anomaly detection to identify spoofed locations

- Mobile infrastructure that automatically configures itself optimizing battery-life and location accuracy for different use-cases over time

- Multi-Region AWS K8s deployment, 99.99%+ availability

Check out our jobs page here: https://radar.com/jobs#jobs If you have any questions, feel free to reply here or you can e-mail me at tim@radar.com

Enveritas (YC S18, non-profit) | Backend Software Engineer | Remote (Global) | https://enveritas.org/jobs/ Enveritas is a 501(c)(3) nonprofit working on sustainability issues facing smallholder coffee farmers. We collect field data in 30+ countries and build systems for analyzing risks in coffee, cocoa, and tea supply chains (including EUDR-related deforestation checks).

* Backend Software Engineer (Python, PostgreSQL/PostGIS, Docker, AWS, Terraform) - $135-$155k -https://enveritas.org/jobs/backend-software-eng/#10d7adef8us (worldwide remote)

Curai | Remote (US) | Full-time | https://curaihealth.com

We're building AI agents to expand access to affordable, and high quality healthcare. We run a vertically integrated primary and urgent care clinic and are actively building an agentic platform focused improving longitudinal care outcomes.

We're hiring engineers to work across patient and clinician experiences, production AI systems, and the platforms that help people discover and access care.

* Sr Software Engineer, Full-Stack: https://jobs.lever.co/curai/46e0dd2f-c1dd-4ca2-9095-5b45f477...

* Sr Software Engineer, Agentic AI: https://jobs.lever.co/curai/cd31b67b-d83c-466a-ac55-cfa4c2ec...

* Sr Software Engineer, Marketing Platform: https://jobs.lever.co/curai/b157298e-4dc7-4594-a9c4-29f0dec4...

Tech stack: Agents SDK, Braintrust, Python, React, React Native/Expo, AWS, Postgres, DynamoDB, Docker, Terraform.

Open Education Applications / Neon | Senior/Lead Platform & DevOps Engineer, Senior Frontend Engineer, Senior Full-Stack Engineer | Utrecht, The Netherlands | HYBRID | DUTCH REQUIRED

Please note: working proficiency in Dutch is strictly required for these roles, and we kindly ask for no automated applications. The rest of this post will continue in Dutch.

Wij zijn een non-profit die zich inzet om lesmateriaal voor scholen beter, betaalbaarder en flexibeler te maken, in zowel digitale als gedrukte vorm.

Werktijden en kantoordagen zijn flexibel en worden in overleg bepaald op basis van de rol. Wij bieden een competitief salaris.

Technologiestack: Git monorepo, TypeScript, Yjs en React, een beetje Python, OpenTofu/Terraform, Scaleway, managed PostgreSQL en managed Kubernetes.

Openstaande rollen:

- Senior/Lead Platform & DevOps Engineer

- Senior Backend Engineer/Architect

- Senior Frontend Engineer

- Senior Full-Stack Engineer

E-mail jobs[at]openeducation.foundation met (HN) + de functienaam in het onderwerp, samen met een korte introductie en een link naar je LinkedIn-profiel en/of GitHub. Als er van beide kanten een goede match lijkt te zijn, plan ik graag een kennismakingsgesprek in.

https://openeducation.foundation | https://www.neon.nl

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.