• 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

Course number and title: ISTEP-FTL: Technical Foundations of Leadership Course description: Technical expertise alone is not enough for engineers to lead effectively in today’s collaborative and global work environments. This short course introduces early-career engineers to the foundational practices of technical leadership: the ability to influence, communicate, and contribute to high-performing teams while applying engineering ways of thinking.   Through a series of online and asynchronous modules, learners will explore how leadership can be developed as a set of learnable skills and behaviours. Topics include leadership identity and self-awareness, team dynamics and psychological safety, equity, diversity and inclusion, communication for technical and non-technical audiences, feedback practices, and influence without formal authority. The course also examines how global and intercultural perspectives shape engineering teamwork and decision-making.   By the end of the course, learners will have developed greater self-awareness as emerging technical leaders and gained practical tools for contributing to effective, inclusive, and collaborative engineering teams.   Learners will apply course concepts through reflective and practical assignments, including: - a technical leadership self-assessment, - a case scenario analysis, - and a final leadership project in which they design a 90-day action plan to address a real or potential challenge in an engineering environment.  Students whose assignment(s) do not meet standards will be provided with opportunities for revision. The course will be offered on a credit/no credit basis. Posting end date: April 22, 2026 Number of Positions (est): One (1) position Estimated TA support: N/A Estimated course enrolment: ~40 learners Schedule: Timing of submissions, and subsequently the review and evaluation of assignments, will vary. The Evaluator/Instructor must be available to review and provide feedback on assignments within two weeks (14 business days) of receiving the submission(s). Sessional dates of appointment: May 4, 2026 – August 31, 2026 Hours: up to 90 hrs Salary: CUPE minimum salary rates are: Writing Instructor 1 - $55.46/hr plus 4% vacation pay; Writing Instructor 1 Long Term - $59.62/hr plus 4% vacation pay; Writing Instructor 2 - $59.62/hr plus 6% vacation pay; Writing Instructor 2 Long Term - $61.05/hr plus 6% vacation pay; Writing Instructor 2 (priority) - $61.38/hr plus 6% vacation pay and Writing Instructor 2 (priority) Long Term- $62.82/hr plus 6% vacation pay. Should rates stipulated in the Collective Agreement vary from rates stated in this posting, the rates stated in the Collective Agreement shall prevail. Minimum Qualifications: Candidates should have at least a Master’s degree in an appropriate discipline (such as, but not limited to, Leadership, Communication, Engineering, Education) with strong written and oral communication skills, and a demonstrated commitment to teaching leadership and communication and demonstrated ability to work independently and part of a team. Preferred Qualifications: A PhD in an appropriate discipline (as above), and/or familiarity with engineering leadership frameworks and engineering communication practices, or other experience in Engineering education and practice are highly desirable assets. Description of duties: The Micro-Credential Evaluator/Instructor will be required to provide constructive feedback to learners on submitted assignments. Duties include: - attend course planning meetings with lead instructor and Micro-Credential Implementation Team - provide written, online feedback on submitted assignments (2 mid-course assignments and 1 end-of-course assignment) - respond online to student enquiries on assignments and provided feedback Application Procedure: Applicants should submit: - Unit 3 application form - Application documents must be submitted via the link found in the application form in one (1) file.   If during the application and/or selection process you require accommodation due to a disability, please contact Chanelle Small-Reid at istep.academic@utoronto.ca.     This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.      It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.        Preference in hiring is given to qualified individuals advanced to the rank of Writing Instructor 2 or Writing Instructor 2 (Priority) in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement. Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement and should not apply for positions posted under the Unit 3 collective agreement.  Candidates who are members of Indigenous, Black, racialized, and 2SLGBTQ+ communities, persons with disabilities. and other equity deserving groups are encouraged to apply, and their lived experience shall be taken into consideration as applicable to the position. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.