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ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE TEAM The Applied AI team builds the agents that show the world what's possible with LangChain. We ship open source reference agents like Open SWE, Open Canvas, and our Deep Research agent that developers across the community use as starting points for their own production systems, while also building internal agents that power LangChain's own GTM and engineering workflows. It's a small, fast-moving team that operates at the frontier, iterating rapidly, running rigorous evals on our own work, and feeding hard-won learnings back into the platform. If you want to work on the frontier of agent-building, this may be the team for you. ABOUT THE ROLE We’re hiring fullstack Applied AI Engineers to help us build AI agents that power every part of LangChain from Marketing and GTM to Recruiting, Support, Internal Tools, and our Core Product. In this role you will own a problem space and work closely with that function to design, build, and deploy production-grade agents, workflows, and applications that transform how we operate. Your work will directly accelerate LangChain’s mission to make intelligent, autonomous software a reality both internally and for our customers. Some of these projects will be open source, contributing to the LangChain and LangGraph ecosystem and setting new standards for how companies build with AI. *This role will be based in our San Francisco or New York office. Employees within commuting distance work from the office are five days per week. Candidates who live outside commuting distance (e.g. >1hr each way), may be eligible for hybrid arrangements depending on location and role requirements. WHAT YOU WILL DO - Design, implement, and deploy end-to-end AI workflows and agents that solve real problems across multiple business domains. - Develop and iterate on agent architectures, evaluation pipelines, and performance frameworks to ensure reliability and measurable outcomes. - Translate emerging AI research and tooling into practical, production-ready solutions. - Communicate technical decisions, trade-offs, and insights clearly to both technical and non-technical stakeholders. - Collaborate cross-functionally embedding with teams like Marketing, GTM, Recruiting, or Product to identify opportunities for agent-driven automation and measurable business impact. - Contribute to the LangChain and LangGraph ecosystem, including open source components, documentation, and shared tools. WHAT YOU WILL BRING - Experienced software engineer with a strong track record shipping AI or ML-powered applications (typically 3+ years, including at least 1 year building LLM systems in production). - Hands-on experience implementing ev

Your work will change lives. Including your own.  The Impact You’ll Make Recursion is leading an era of autonomous science – iterating across the discovery process, leveraging machine learning and agents built-for-purpose to uncover novel insights across biology and chemistry, fueling our clinical-stage pipeline. As the Senior Product Manager for Agentic Science, you will sit at the intersection of drug discovery and AI, serving as the critical translator between our scientific teams and the agentic systems they depend on. Your primary focus will be on outcomes: ensuring our agents impact Recursion’s pipeline through demonstrably accelerating discovery. This role is not about maintaining a static roadmap; it is about navigating the frontier of a rapidly evolving field. You will partner closely with drug discovery scientists to understand their workflows, translate their needs into agent task definitions, and design and maintain the benchmarks that -ensure our agents are driving value. As the field evolves rapidly, you will help the team distinguish real scientific progress from superficially promising results. In this role, you will: - Champion Benchmarking for Agentic Science: Drive alignment across scientific and technical teams around evaluation frameworks that measure agent performance against scientifically meaningful outcomes, continuously refining them as the field evolves. - Drive Outcome-Focused Product Development: Keep the team anchored to what matters: building agents that meaningfully advance drug discovery programs, not just executing tasks. - Evangelize the "Human-in-the-loop" Evolution: Work with scientific stakeholders to define interfaces where humans review, validate, and shape agent reasoning, ensuring our scientists evolve from "operators" to "architects" of discovery. - Monitor the Competitive Benchmark Landscape: Track how leading organizations across pharma AI, biotech, and foundation model research are measuring agentic performance. Ensure Recursion's evaluation frameworks stay calibrated against external standards, so our benchmarks reflect genuine scientific progress rather than internally optimized metrics. The Team You’ll Join  You will join a cross-functional team of software engineers, data scientists, AI/ML scientists and drug discovery biologists and chemists who build the technical bedrock that enables autonomous science, including agent orchestration, guardrails, and the connectivity between our digital and physical assets. You will work closely with the Discovery teams (the users of these agents), the AI Research teams (who build cutting-edge models), and our automated biology and chemistry lab teams (who generate the data that feeds into the models). The Experience You’ll Need - Background in Drug Discovery or AI-driven Science: You have direct experience working in drug discovery, biotech, or AI-driven scientific research. Ideally, you will have worked hands-on with agentic systems (or agents) in a scientific context. You can credibly partner with PhD-level scientists and translate between scientific goals and technical systems without losing fidelity on either side. - Fluency

Your work will change lives. Including your own.  The Impact You’ll Make As a computational biology specialist on our Target ID team, you will be at the forefront of transitioning Recursion to its next era of drug discovery. You will serve as a critical biological anchor for a highly technical data science team building the next generation of our target discovery pipelines. The team’s mission is to identify novel therapeutic targets at scale across the genome for hundreds of indications. You will have access to all of Recursion's data layers—including massive internal maps, functional genomics, and rich patient data (transcriptomics, genetics, and Real-World Data/EHR)—and will be tasked with proposing, piloting, and deploying new methods to integrate these datasets. Crucially, you will leverage your biological subject matter expertise to orient and focus your data scientist and software engineer peers towards reliable and correct usage of biological data and feasible candidate programs. As we build out semi-automated and agentic tools (e.g., multi-agent LLM systems for portfolio oversight and target assessment), your deep real-world biology experience will guide the development of these tools, ensuring they are grounded in biological reality and translate to meaningful portfolio and patient impact. The ideal candidate is a computational biology specialist with high fluency in data science tech stacks. You know how to build and evaluate predictive models and build agentic pipelines, and your differentiator is your deep understanding of patient-relevant datasets, disease biology, and target discovery. In this role, you will: - Discover & Evaluate: Propose and validate novel targets using deep integration of patient data (transcriptomics, population genetics, EHR, etc) and Recursion's internal multi-omic data layers. - Guide & Automate: Collaborate closely with data scientists to build, refine, and guide semi-automated and agentic target discovery tools. You will ensure these tools are biologically sound and can scale to evaluate hypotheses across many indications spanning a wide range of therapeutic areas. - Triangulate: Use advanced statistical methods (e.g., causal inference, survival modeling) to establish confident target-to-patient connections and define specific addressable patient populations for early pipeline programs. - Bridge the Gap: Translate complex platform findings into disease-relevant applications, bridging the gap between high-dimensional data science output and actionable drug discovery insights. - Present & Influence: Communicate complex biological rationale and data analyses to decision-makers and cross-functional stakeholders, driving data-backed "go/no-go" decisions in a two-stage target approval process. The Team You’ll Join Our group is a bold, agile, diverse collective of data scientists and computational biologists driving Recursion’s early portfolio strategy. We are focused on aggressively expanding our early-stage pipeline with highly validated, novel therapeutic targets. To achieve this, we prioritize defining specific patient populations and establishing clear, data-backed translational paths from day one of every new program. Because this

ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE ROLE LangChain Academy is how developers, engineers, and AI practitioners go from curious to capable on LangChain’s products. We’re looking for an Education Engineer to own the development of curriculum that turns developers into proficient agent builders. This is a high-craft, high-ownership role at the intersection of software engineering and education. You’ll design and build the courses, tutorials, and live workshops that teach the LangChain ecosystem to a community of over 1 million developers — many of whom are building production agents for the first time. The bar is high: developers can tell the difference between content that genuinely helps them build and content that’s just a feature tour. Your job is to create content that consistently clears that bar. You’ll work closely with LangChain’s engineering team to stay at the frontier of what’s possible with LangGraph, LangChain, and the broader ecosystem, and work with the Technical Content Manager to shape curriculum strategy and prioritize what gets built. You’ll also partner with the Education Marketing Lead on course launches, YouTube content, and live events. WHAT YOU’LL DO CURRICULUM & COURSE DEVELOPMENT - Build the courses that define how developers learn to build agents. Design and develop end-to-end curriculum for LangChain Academy - from scoping and structure to hands-on labs and assessments — that take developers from zero to proficient with our products. - Partner with LangChain engineers to stay at the frontier. Work closely with the engineering team to understand what’s new, what’s changing, and what developers need to know. Translate that into curriculum. - Write code that teaches. Build the notebooks, example apps, and working code samples that sit at the core of every course. The code needs to be clean, well-structured, and genuinely instructive. - Build the conceptual foundation developers need to build well, not just build. As coding agents handle more of the implementation, what developers need most is a clear mental model of what’s possible and what good looks like — the right architecture, the right tradeoffs, the right patterns. Craft explanations, diagrams, and frameworks that develop genuine understanding. - Keep curriculum current. Agent engineering is a fast-moving field. Own the ongoing review and revision of existing courses to keep them accurate, relevant, and aligned with the latest LangChain releases and agentic applications. LIVE EDUCATION & COMMUNITY - Represent LangChain at workshops, meetups, and conferences. Design and deliver live technical education at developer events — from intimate hackathons to large conference stages. Be the p

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. Think of us as doing "neuroscience" of neural networks using "microscopes" we build - or reverse-engineering neural networks like binary programs. More resources to learn about our work:  - Our research blog - covering advances including Monosemantic Features and Circuits - An Introduction to Interpretability from our research lead, Chris Olah - The Urgency of Interpretability from CEO Dario Amodei - Engineering Challenges Scaling Interpretability - directly relevant to this role - 60 Minutes segment - Around 8:07, see a demo of tooling our team built - New Yorker article - what it's like to work on one of AI's hardest open problems Even if you haven’t worked on interpretability before, the infrastructure expertise is similar to what's needed across the lifecycle of a production language model: - Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips - Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model's internal activations mid-forward-pass - for example, adding a "steering vector" - Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission The science keeps scaling - and it's now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI. Responsibilities: - Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector a

Scale is at the frontier of the AI industry, improving the world’s leading generative AI and large language models through model evaluations, human-powered supervised fine-tuning datasets, world-class reinforcement learning with human feedback, and more.&nbsp; &nbsp; Scale AI’s Public Sector team is growing in the Generative AI space, and we’re seeking an Strategic Projects Lead to own high-impact projects that drive revenue and experimentation. In this role, you’ll work across operations, engineering, and customer engagement to produce world-class training and test and evaluation data for Large Language Models for our Public Sector customers.&nbsp; &nbsp; This role offers a rare opportunity to make a meaningful impact at the intersection of AI and national security. You will help build Generative AI data-labeling pipelines from the ground up, create operational processes to manage and optimize an in-house expert data workforce, and develop novel technology-driven approaches (e.g., scripts, prompt engineering, hybrid data) to improve the quality of our training and evaluation datasets. In addition, you will partner directly with our internal machine learning experts and external stakeholders to ensure our data enables the development of mission-critical applications of AI. &nbsp; You will: - Develop, build, and maintain the infrastructure required to ensure data pipelines are efficient, scalable, and produce high-quality outputs - Take ownership of day-to-day progress on high-priority data production pipelines, ensuring projects move forward efficiently - Partner with subject matter experts in their fields to validate the quality of our data and to translate deep domain knowledge into scalable processes and measurable outcomes &nbsp; - Work closely with customers to understand their requirements and design data taxonomies that optimize model performance. - Utilize analytics and data visualization tools to track progress, identify bottlenecks, and make data-driven decisions to optimize pipeline performance - Influence cross-org collaboration to define and advance human data strategy, influencing technical and non-technical stakeholders to ensure data quality, scalability, and long-term platform leverage - Own larger and larger components of our data delivery processes, until you ultimately serve as the full owner of our most visible and high impact customer pipelines &nbsp; You have:&nbsp; - 5+ years of experience in product development, data science, or operations - A history of successful project management and comfort in ambiguity - Ability to analyze complex operational data, build queries, and identify trends to inform decisions and optimize processes - Technical aptitude to understand how to produce data for state of the art post-training techniques such as supervised fine tuning (SFT), reinforcement learning through human feedback (RLHF), Reinforcement Learning with Verifiable Rewards (RLVR) etc&nbsp; &nbsp; Nice to have:&nbsp; - Experience working in defense tech and/or an AI company - A technical degree in fields like computer science, data science, or engineering - A deep understanding of ML operations for generative AI workflows / products - An active Top Secret security clearance&nbsp; <div class="content-pay-transparenc