Search Atlas | Product Engineer (Product Manager role, not a Full-stack role) | REMOTE (anywhere, EST hours) | Full-time contractor | $23-$34 USD/hour | https://careers.searchatlas.com/jobs/599248-product-engineer...

We're ~160 people, fully remote, building an AI marketing platform. Our main product is OTTO, which runs a site's SEO on its own - it reads live search data and pushes the technical and on-page fixes itself, nobody queues them up. Around that we've got ad automation for Google and Meta, GBP management for multi-location brands, content gen, and LLM visibility tracking (whether ChatGPT and co are citing you). Autotrader, Hyundai, P&G, Vendasta are some of our customers.

Work happens in pods, 1 PM and 2-4 engineers. You own your pod's roadmap and ship it with them, no veteran PM signing off on your specs first. That's the part most PM roles won't hand you.

Looking for 3+ years PM in B2B SaaS with an actual technical background - eng or QA before PM, or a PM who's been getting into the codebase themselves. You should be able to read Python and TS, write your own SQL, find your way around an API, and not need an engineer to tell you what a stack trace says. We're Django on the back, React/Next on the front. QA is yours end to end including release sign-off, people tend to underestimate that bit. And you're already using Claude Code or similar daily, not "open to it".

A few notes before you apply:

- Experience: we're looking for people with a few years of hands-on product or engineering work behind them. The role is autonomous from the first week, so we need you ready to take the wheel. - Recruiting agencies: we hire in-house and manage every application ourselves, so there's no need to send candidates or reach out about partnerships. Thank you for understanding! - If your stack matches, please do reach out. Django/DRF/Celery/Postgres with React/Next, Python automation work around SEO: if that's your day-to-day and you're using AI coding tools (Claude Code, Codex, Cursor, whatever you like) in your real workflow, I want to hear from you. Don't talk yourself out of it because the title isn't an exact fit; tell me what you've built.

Recruitment process is screen with me, a take-home that runs about 75 mins, technical round with one of our senior PMs, then a call with our Director of Product (who you'd report to). I usually come back to people within 2 business days.

Our ATS forces us to list specific ywhere as long as you can do EST hours, so apply even if yours isn't

Apply at the link and mention HN somewhere, in your pitch or wherever it fits. joao.pedro@searchatlas.com if you need me, though the link's faster.

P.S: Not a PM, or the PM role isn't quite your thing? Have a look at everything else we have open at https://careers.searchatlas.com and apply directly to the role that suits you best (preferred route). If you're not sure which one that is, email me with a short line on what you do and I'll point you to the right one.

RINSE | REMOTE (US or Canada) or San Francisco, Los Angeles, Chicago, Boston, New York City, New Jersey, Seattle, Austin, Toronto, or Washington DC | Software Engineer | Full-Time | Salary: $80k-$200k USD | https://www.rinse.com

Rinse provides dry cleaning and laundry delivery services to customers in twelve metropolitan areas in the US and Canada. We have sophisticated logistics optimization software, a polished consumer product, and firm business fundamentals. We're now over a decade old - this is a stable, yet consistently growing and innovating, company.

Our engineering team is distributed across the United States and internationally, and has been entirely remote for years now, but a desk can be provided in the above cities if you'd prefer.

We're open to both newly-graduated engineers or more senior engineers, provided they meet our bar. We're particularly interested in true full-stack engineers, with strong React / Typescript / CSS skills to complement comfort in a Python / Django environment. We're also seeking engineers with experience leveraging AI to automate complex operational processes.

This season, we're particularly interested in new grads or junior engineers, to round out the team.

We're also looking for people with experience manipulating video data at scale, for computer vision applications. Similarly, we're interested in adding people with experience building and managing MCP services.

You will join a high-caliber team where collaboration, honesty, and open communication are at the core of everything we do.

Search term bingo: Logistics, Django, Python, Optimization, React, React Native, Postgres, Mobile Engineer, Automation, Delivery, OR-tools, Forecasting, Vision, Physical AI, MCP

Location: Boston, MA Remote: Yes (preferred) Willing to relocate: No Technologies: Python, TypeScript, Rust, SQL | pandas, polars, Arrow, DuckDB/DataFusion, Parquet, PostgreSQL | React, ag-grid, FastAPI, Django | MCP servers, LLM tool design, agent evals Email: paddy@paddymullen.com

I build data tools, from the dataframe backend to the browser.

I created and maintain Buckaroo (https://github.com/paddymul/buckaroo), an open source dataframe UI for Jupyter, marimo, and VSCode — summary stats and a histogram over every column, across pandas and polars. 683 stars. Scrolling millions of rows without eating the browser took a segment-merging row cache and parquet decoded client side.

Recently: Tallyman, a data science environment for coding agents. Named computations in a dependency DAG, results rendered live in a browser companion, every step keeping the prompt that made it. Demoed at PyData London 2026.

Before that: Flatfile, Attune, Tudor, and Bokeh/Wakari at Anaconda.

Looking for data infrastructure, developer tools, or agent tooling — where a hard backend problem has to surface as something a person can use.

Octane | Senior Software Engineer, Decisioning | REMOTE (US only) or NYC/Dallas | $130K to $170K base + bonus eligibility + employee stock options

Octane is unlocking the power of financial products for merchants and consumers. Our cutting-edge technology and innovative financial products empower businesses with more control and flexibility, enabling them to deliver seamless digital experiences, drive customer loyalty, and build long-term value.

We are hiring a Senior Software Engineer to build features and infrastructure for our decisioning (automated underwriting) team. Used by dealerships across the country, this product provides a streamlined workflow for dealerships to get fast, flexible financing to their customers. Working with a team of passionate, experienced engineers, you'll work throughout the backend of our stack with technologies such as Python, Django, PostgreSQL.

Apply: https://octane.co/o/who-we-are/careers/jobs-open/?gh_jid=799...

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.