Time since we first saw it
Day 164
Up longer than most. Similar jobs usually come down between day 4 and day 77.
From 51 similar postings that came down. Coming down is not always a hire.
Founding AI/ML Engineer
Reads like an AI Engineer role: building products on existing AI models.
San Francisco, CA
Open 5 months, first seen by us 164 days ago- Pay
$150,000–225,000/year · BaseAnnual period assumed — pay source
Compensation & Benefits Base salary range: $150,000 – $225,000 and equity, depending on experience and expertise Competitive compensation package including equity
Read the full posting- Work setting
On-site — work setting source
Work setup in the employer’s posting: IN_OFFICE
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingDesign and build retrieval and memory systems using vector databases and semantic search
Build and maintain RAG pipelines that combine structured and unstructured data
Develop feedback loops and evaluation systems to measure and improve model output quality
Explore reinforcement learning and fine-tuning to adapt model behavior to user context
Integrate and compare multiple LLMs and APIs, picking the right one for each task
From the employer’s posting
Design and implement retrieval and memory systems using vector databases and semantic search
Build and maintain RAG pipelines that combine structured and unstructured data
Develop feedback loops and evaluation systems to measure and improve model output quality
Explore reinforcement learning (RL) and fine-tuning approaches that adapt model behavior to user context
Integrate and experiment with multiple LLMs and APIs, choosing the right tool for each task
Benefits in the posting
Full benefits wording- Base salary range: $150,000 – $225,000 and equity, depending on experience and expertise
- Competitive compensation package including equity
- Comprehensive medical, dental, and vision coverage
- Flexible PTO and a work culture built on trust and autonomy
From the employer’s posting.
About Hellobabs-Ai
Our mission is to give people the power to live organized lives and organized minds.
In the employer’s words · Read in context
Posting record
Report an errorOpen 5 monthsWe first saw this posting 164 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026164 days ago
- Recorded sightings
- 74
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Hellobabs-Ai is hiring
1 open job
- No new data jobs since June
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
Where the posting names them
- 4 or more years of experience working with LLMs, machine learning infrastructure, or applied AI systems - Strong experience with Python and frameworks like LangChain, LlamaIndex, or equivalent orchestration tools - Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)
Your next step
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Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Job description
About Babs
Babs is building the smart operating system for modern families. Most AI tools are designed for work life, but managing home life is its own full-time job filled with coordination, communication, and constant context switching. Babs acts as a second brain that brings order to everyday life by connecting calendars, tasks, and messages into one intelligent system.
Our edge is in our ability to make complex systems feel simple. Our strength is our team — driven, thoughtful builders united by a mission to bring order to everyday life and give people the clarity of an organized mind. Our mission is to give people the power to live organized lives and organized minds. Our vision is to build joyful, connected communities. That begins with creating systems and workflows that help people feel more present and effective in their daily lives. When households run smoothly, they have more capacity to connect, contribute, and strengthen their communities.
The Role
We’re looking for an Founding AI Engineer to design, build, and scale the intelligence layer that powers Babs. You’ll work on the infrastructure that connects large language models, vector databases, and real-world data into a seamless, adaptive system.
This role sits at the intersection of machine learning infrastructure, data systems, and applied product engineering. You’ll be responsible for how Babs learns, remembers, and improves — turning context into intelligence and feedback into reinforcement.
You’ll help us move from AI-assisted features to a truly AI-native product that feels personal, contextual, and trustworthy.
What You’ll Do
- Design and implement retrieval and memory systems using vector databases and semantic search
- Build and maintain RAG pipelines that combine structured and unstructured data
- Develop feedback loops and evaluation systems to measure and improve model output quality
- Explore reinforcement learning (RL) and fine-tuning approaches that adapt model behavior to user context
- Integrate and experiment with multiple LLMs and APIs, choosing the right tool for each task
- Collaborate with platform and product engineers to bring intelligence into real features
- Create observability and evaluation systems for AI latency, accuracy, and reliability
- Contribute to architectural decisions that shape Babs’ long-term AI infrastructure
Our Ideal Candidate Has
- 4 or more years of experience working with LLMs, machine learning infrastructure, or applied AI systems
- Strong experience with Python and frameworks like LangChain, LlamaIndex, or equivalent orchestration tools
- Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)
- Familiarity with evaluation frameworks, feedback loops, and reinforcement learning techniques
- Experience building and scaling data or ML pipelines in production environments
- Curiosity about user behavior and how models can be tuned to better serve real human needs
- A thoughtful, practical approach to experimentation and iteration — you ship and learn
Bonus Points For
- Experience designing model evaluation frameworks or AI Evals
- Contributions to open-source AI infrastructure projects
- Familiarity with prompt optimization, tool calling, and agentic workflows
- Experience with multi-modal models (text, image, or speech)
- Knowledge of GCP, Firebase, or event-driven architectures
- A background in consumer or productivity products
Compensation & Benefits
- Base salary range: $150,000 – $225,000 and equity, depending on experience and expertise
- Competitive compensation package including equity
- Comprehensive medical, dental, and vision coverage
- Flexible PTO and a work culture built on trust and autonomy
- A team that values craftsmanship, collaboration, and purpose over fluff
If you’re excited by the idea of building the intelligence layer behind a product that helps people live with more clarity and calm, we’d love to meet you — even if you don’t check every box. We care about curiosity, integrity, and a shared belief in what we’re building.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Compensation & Benefits Base salary range: $150,000 – $225,000 and equity, depending on experience and expertise Competitive compensation package including equity
- Location & working pattern
San Francisco, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.