Doowii is building an “AI operating layer” for education analytics: connect your institution’s systems (SIS/LMS/CRM), and non-technical teams can ask questions, explore, and take action with integrated workflows and highly customized data ontology.
We’re hiring SWEs to ship AI-first product experiences and infrastructure.
What you’ll build:
- LLM-backed app features: multi-step query/reasoning flows, conversational UX, follow-ups, citations, and “show your work”
- Evaluation + reliability: offline eval sets, regression testing for prompts/tools, quality metrics, guardrails, and latency work
- Agentic workflows: tool-calling, retrieval over structured sources, workflows that can debug/repair queries and guide users to the right next step
- App/platform foundations as needed: APIs, auth/RBAC, job orchestration, observability, and cost controls
Stack (approx): Python, TypeScript, modern web app stack, LLM tool-calling/RAG patterns, GCP.
You might be a fit if:
- You’ve shipped real product features (backend + app), and care about UX + correctness
- You have experience building LLM-enabled systems (or you’ve built strong distributed/backend systems and want to go deep on applied AI)
Doowii | Full Stack Engineer | Remote (US) | Full-Time
Doowii is on a mission to empower educators with AI-driven data analytics solutions that enhance educational outcomes. We’re a rapidly growing EdTech startup that recently closed a $4.1M fundraising round backed by GSV Ventures, Better VC, Avesta Fund, and more (press release: http://bit.ly/3Rfodmt)
What We Do:
-Provide AI-driven data analytics solutions for schools/universities, essentially serving as an AI data scientist for educators.
-Seamlessly integrate advanced data analytics into major EdTech platforms through partnerships.
About the Role: We’re looking for a Full Stack Engineer who is passionate about making a difference in education. You'll work on developing and maintaining scalable web applications using a modern techstack. Your role will involve collaborating with other engineers to design and implement new features, optimizing our applications for genAI accuracy, speed, and scalability, and contributing to a high standard of code quality. Needs to have the startup hustle mindset.
Doowii | Full Stack Engineer | Remote (US) | Full-Time
Doowii is on a mission to empower educators with AI-driven data analytics solutions that enhance educational outcomes. We’re a rapidly growing EdTech startup that recently closed a $4.1M fundraising round backed by GSV Ventures, Better VC, Avesta Fund, and more (press release: http://bit.ly/3Rfodmt)
What We Do:
-Provide AI-driven data analytics solutions for schools/universities, essentially serving as an AI data scientist for educators.
-Seamlessly integrate advanced data analytics into major EdTech platforms through partnerships.
About the Role:
We’re looking for a Full Stack Engineer who is passionate about making a difference in education. You'll work on developing and maintaining scalable web applications using a modern techstack. Your role will involve collaborating with other engineers to design and implement new features, optimizing our applications for genAI accuracy, speed, and scalability, and contributing to a high standard of code quality. Needs to have the startup hustle mindset.
At Doowii (doowii.io), we're on a mission to change education with data. We need a Senior Backend Software Engineer who's eager to tackle backend challenges, from data engineering to ETL processes, and help us empower educators with actionable insights. If you're into building scalable data pipelines and making a real impact, let's talk.
What You'll Do:
Lead backend projects to enhance our data analytics platform.
Design systems that are scalable, secure, and efficient.
Navigate the complex world of data with autonomy and creativity. We design our data pipelines to work with LLMs.
Must-Haves:
- Bachelor's in Computer Science (or related) with 5+ years in engineering.
- Skills in Python, Java, SQL, plus cloud and server management know-how.
- Independent problem-solving and a knack for cutting through ambiguity.
If you've got a Master’s, are into EdTech, have startup experience, or have dabbled in advanced data analytics tech, we're especially keen to hear from you.
- Competitive pay ($150K-$190K), equity, unlimited PTO, and comprehensive benefits from day one.
- A mission-driven culture that’s all about improving education.
I don't understand the obsession with ClickHouse. While it seems like it fits this particular use case, it still deals with the same limitations and challenges of columnar DBs. Your queries will be very fast with counts/averages, but there's a tradeoff with other functions: inserts are efficient for bulk inserts only, your deletes and updates are slow, no secondary indexes...
While Clickhouse can be lightning fast, is it really designed to be a main backend database?
Can someone explain how this is more powerful than someone use an Python/R based workflow? E.g., I currently use a combination .ipynb, python scripts, and RStudio and this feels like it covers everything I need for any data science project.
I had this exact same thought when I read the headline. It seems like MS and others are viewing ML as a similar opportunity to Big Data/BI ten years ago. You saw the "democratization of data" as people with little technical skills could suddenly create analytics dashboards within tools like Tableau.
In my opinion, it's far too easy to make a critical mistake during design/implementation of ML to follow this same path. And what's more, if you mess up making an analytics dashboard, it's usually fairly obvious. In ML, there are MANY ways to mess up a model and you have no easy way to tell.
If someone doesn't have the technical experience behind creating these models, I would not trust any output they give me from using one of these tools. And if they do have the experience, they would certainly not be choosing to use one of these tools either.
A lot of that speculative growth comes from Turkey's comparatively younger population (hence more productive hours worked each day), and their continued population growth. Germany's demographics are sliding the other way, with an aging and stagnating population.
Are you sure? 25,000 employees * 12 months * 1000 = 300 million in economic activity.
Obviously not all 25000 employees would get an extra 1k, but the point being...these things can scale significantly when you're talking about Amazon. It's not a trivial thing for SMBs to fill the void that Amazon is leaving.
The issue I'm having with this comparison is that you are equating the "nights stayed" with the points earned (the more accurate currency).
>They each have a rewards program which grants one free night for every night stayed.
It's a lot more fair to say for every $1000 spent, you have enough points to redeem a free night. In which case, some people might choose to spend more nights at the cheaper Fairfield Inn, and some might choose to stay fewer nights at the luxury Ritz. The bad money is not driving out the good.
It's true when it comes to qualifying nights, people will try to hit elite night requirements at cheaper properties. But that's not driving out the business for higher-end properties. Especially because award redemption is (mostly) based on how much you spend, not how many nights you stay.
This sounds like a misinterpretation of Gresham's Law. That's specifically talking about currency, which I suppose in this case would be the reward points. There is no "good" vs "bad" version of the currency though. Certain types of points are not driving other types of points out of circulation. The value of the points depends on how you use them, with people more eager to spend points on high-value redemptions.
ride sharing isn't exactly very profitable at the moment. I don't think the revenue would offset the additional engineering/product costs of just running the rideshare service, much less the cost of testing.
Author here: please feel free to ask any questions/comments on our methodology. I designed the neural net, which is basically video classification mapped to bitrate-ladders. Interested in any feedback!
Doowii is building an “AI operating layer” for education analytics: connect your institution’s systems (SIS/LMS/CRM), and non-technical teams can ask questions, explore, and take action with integrated workflows and highly customized data ontology.
We’re hiring SWEs to ship AI-first product experiences and infrastructure.
Roles: - Senior Software Engineer (AI Product / Backend) - Software Engineer (Full-stack, AI) - Implementation Engineers (FDE or CXE)
What you’ll build: - LLM-backed app features: multi-step query/reasoning flows, conversational UX, follow-ups, citations, and “show your work” - Evaluation + reliability: offline eval sets, regression testing for prompts/tools, quality metrics, guardrails, and latency work - Agentic workflows: tool-calling, retrieval over structured sources, workflows that can debug/repair queries and guide users to the right next step - App/platform foundations as needed: APIs, auth/RBAC, job orchestration, observability, and cost controls
Stack (approx): Python, TypeScript, modern web app stack, LLM tool-calling/RAG patterns, GCP.
You might be a fit if: - You’ve shipped real product features (backend + app), and care about UX + correctness - You have experience building LLM-enabled systems (or you’ve built strong distributed/backend systems and want to go deep on applied AI)
Comp: competitive salary + meaningful equity (seed-stage)
Email: [email protected] Include: resume/LinkedIn