About Us We’re building the future of writing development. We believe that writing is thinking, and thinking changes life outcomes. Our mission is simple: help more students learn how to express ideas clearly, confidently, and independently. We believe AI should support learning, not shortcut it. That belief shapes everything we build: tools that strengthen skills, respect the role of educators, and keep students doing the hard (and important) work of thinking for themselves. We’re a fast-moving team working at the intersection of education, design, and AI — building things that are used in real classrooms, by real teachers, with real impact. If you care about craft, learning, and building technology that actually matters, you’ll feel at home here. About the Role We’re hiring an AI-Native Product Engineer to build and ship high-quality frontend products powered by modern AI workflows. This is not a traditional frontend role — you’ll use agentic AI systems (multi-step agents, tools, evals) to accelerate development, while still owning clean, production-ready code when it matters. Ideal for engineers who enjoy building fast, thinking independently, and working at the intersection of product, AI, and user experience. What You’ll Be Doing - Build and ship frontend features using React - Use AI workflows (agents, tools, evals) to accelerate product development - Design structured AI systems — not just prompt in chat - Step in to debug, refine, and own code when AI falls short - Integrate APIs (including LLM services) and collaborate with backend teams - Improve UX, especially around interactive and content-heavy workflows - Ship clean, maintainable, production-ready code What We’re Looking For Core Requirements - Strong React + JavaScript/TypeScript fundamentals - Able to build and ship features independently - Comfortable working with APIs and backend integration - Good product sense + UI/UX awareness - Clear communicator, able to work in a remote setup AI-Native Mindset - Experience using AI beyond basic prompting (agents, workflows, tools) - Understands how to make AI outputs structured, testable, and reliable - Knows when to rely on AI — and when to take over manually Bonus - Angular exposure (for existing systems) - Experience with rich-text editors (e.g. Lexical) - Interest in EdTech / learning products Candidates should be able to show: - GitHub / live projects / portfolio - AI workflows or systems they’ve built - Real examples of shipped work

• Build and maintain production-grade AI agents that take action across ANNA's clinical and operational workflows — scheduling, intake, RCM, clinical decision support — using LLM reasoning over our shared data ontology. • Design and implement tool use, state management, and failure handling for multi-agent workflows, ensuring reliability and quality in production. • Contribute to ANNA's MCP server suite, exposing core data and product surfaces — Salesforce, scheduling, RCM, HR — to AI agents in a standardized, composable way. • Implement and optimize RAG pipelines using pgvector, including embedding strategies and vector search, to surface relevant clinical history at the point of care. • Build and iterate on clinician-facing product surfaces in React, ensuring they integrate seamlessly with ANNA's AI agent backend and fit the real-world workflows of clinical teams. • Own features end-to-end: from backend API design in Django/FastAPI through to the front-end experience, with a high bar for quality at every layer. • Iterate quickly in the open with clinical and operational end users, incorporating feedback and shipping improvements with velocity. • Contribute to ANNA's clinical data ontology — the shared, specialty-agnostic data foundation that powers all AI agents, decision support, and outcome measurement across the platform. • Write clean, well-documented, HIPAA-compliant code and contribute to CI/CD pipelines and engineering best practices as the team scales. • Participate in architecture discussions and help make pragmatic build vs. buy decisions across the stack.

• Building features and experiences from 0-to-1. This is a product-focused role, and you will be shipping regularly. You ideally have experience building early-stage products and are comfortable making technical trade-offs and taking risks. • Design our AI-enabled capabilities. We are building out a set of application capabilities on top of foundational models, and you will be critical to determining how we structure those for adaptability, reuse, and scalability. • Work with coaching to help translate coaching insights into AI Agents. Mento has a tremendous advantage in bringing AI coaching to life - an extremely strong team of active coaches. Part of this role will be figuring out how to translate coaching workflows into high-quality product experiences. • Build pipelines and integrations to import and export data into the product. Our product connects into a wide ecosystem of productivity and learning and development tools. You will help us design and build these integrations scalably and reusable. • Educate the team on best practices around data and AI. We have a small, fluid team filled with experts but not silos. As an expert bringing AI and data expertise, you will be expected to share your knowledge with the rest of the team via peer mentorship, code reviews, lunch and learns, and more.

Bioinformatics Engineer — Single-Cell AI At LatchBio, our AI agents help thousands of scientists analyze and interpret data across the full stack of modern multi-omic technologies — starting with single-cell and spatial, and expanding fast. We're building the ground truth for AI in single-cell biology. Our benchmark scBench — 394 verifiable problems across six sequencing platforms — shows the best frontier model today still fails nearly half the time. We're hiring bioinformatics engineers to close that gap: scientists who can turn real experimental data into the precise, falsifiable questions that define what it means for an AI agent to actually understand scRNA-seq. What you'll do - Own end-to-end scRNA-seq analyses across multiple projects: raw platform outputs → QC and failure diagnosis → normalization → dimensionality reduction → clustering → cell typing → differential expression → trajectory analysis → defended biological claim. - Build reproducible workflows and produce clear decision traces: what was filtered, why, what changed the conclusion, what would falsify the claim. - Distill analysis steps into precise, falsifiable biological questions with single defensible answers — the core unit of our eval suite. - Debug platform and data issues with precision: turn messy results across diverse sequencing chemistries into crisp hypotheses, sanity checks, and a stepwise debugging plan. Requirements (must-have) - Experience with end-to-end data analysis for one or more of the following sequencing platforms: MissionBio, ParseBio, CSGenetics, BD Rhapsody, Illumina, or 10X Chromium - Analyzed 3+ datasets from raw data to end insight for either publications or industry experiments with real world consequences - Working understanding of platform-specific quality control thresholds and intuition for numerical examples of positive or negative results (e.g., 100K cells from a ParseBio run with 80% mitochondrial reads means something is wrong) - Familiarity with the landscape of computational biology tools for scRNA-seq tasks (e.g., Scanpy/Seurat for core workflows, cell typing frameworks like CellTypist or Azimuth, DE methods like DESeq2 or edgeR) - Strong understanding of experimental design, hypothesis generation and scientific conclusions from papers using one of the sequencing platforms described - Ability to distill an analysis step into a precise, falsifiable biological question with a single defensible answer - Working understanding of concepts in statistical inference: hypothesis testing, confidence intervals and/or estimators - Working understanding of important algorithms in high dimensional data analysis: e.g. PCA, neighborhood graphs, UMAP, clustering methods (Leiden/Louvain) Desired experience (nice-to-have) - Published research that relied on modern single-cell RNA sequencing techniques. - Engineered tools or packages in the single-cell biology domain. - Experience generating training data for AI agents or foundation models. Ideal candidate You are a scientifically fluent engineer who has run real scRNA-seq pipelines and knows where they break. You can look at a clustering result and form a biological opinion about it — not just report what the algorithm returned. You're comfortable being wrong, updating beliefs with evidence, and writing down decisions so others can reproduce and critique your work. You think in falsifiable questions and know the difference between a result that's numerically correct and one that's biologically meaningful. Compensation & benefits - $130k–$180k/yr (performance-based) - Equity - Unlimited PTO (truly) - Waterfront office in China Basin, San Francisco - Free lunch and dinner - 100% premium covered on Blue Shield's platinum health plan ($0 premium, $0 deductible) - 401(k) plan options - Work visa sponsorship - Company-sponsored professional development Full-time preferred, part-time available. In-person in San Francisco preferred, remote options available. About the team We work on serious problems at the most important intersection in history: biology and AI. We are building a team of world-class people, and are all eager to dedicate a substantial part of our life to solving these problems. If we succeed we will hugely accelerate scientific progress and aid the creation of therapies for cancer, solutions to global warming, and cures for aging. Who you'll work with - Spatial Biology: Zach, Alex, Ben, & others. - Sr. Bioinformatics Engineers: Zhen, Harihara - Product: Hannah and Nathan - Founders & Chief of Staff: Alfredo, Kyle, Kenny, Jordan How to apply Apply on our Ashby posting here. Hiring process Our process moves quickly, typically completed within one week. - Round 0: Apply with a resume and cover letter - Round 1: Introduction — Saul (Technical Recruiter) - Round 2: Take-Home Project — Zhen and Harihara (Bioinformatics) - Round 3: Technical — Kenny (CTO) Round 4: Culture — Jordan (Chief of Staff) or Kyle (COO) - Offer Learn more Explore our products, read our papers, and engage with our team. - Agent.bio — The AI agent for biology - Benchmarks.bio — The benchmarks for biology agents - Console.latch.bio — The harness for agentic data analysis