Full Stack Software Engineer

Role Overview Anthropic is looking for a Full-Stack Software Engineer with a focus on reinforcement learning. This role centers on building applications that use reinforcement learning to advance artificial intelligence. Work closely with skilled teammates to create software that makes a real impact. Location San Francisco, CA or New York City, NY

About UsAt Preference Model, we are at the forefront of developing advanced training data essential for the evolution of artificial intelligence. While today's AI models exhibit significant power, they often fall short in diverse applications due to limitations in their training data. We specialize in creating reinforcement learning environments that present AI with authentic research and engineering challenges, enabling them to iterate and learn through realistic feedback loops.Our founding team boasts experience from Anthropic’s data department, where we established the data infrastructure, tokenizers, and datasets that supported Claude. We collaborate with top-tier AI research labs to bring AI closer to its groundbreaking potential and are proudly backed by a16z.About the RoleAs a Software Engineer on our team, your responsibilities will include:Designing and Developing Reinforcement Learning Environments: Architect comprehensive simulation platforms that encompass environmental context, task definitions, and reward functions to facilitate AI agents' learning and performance of intricate tasks.Building Robust Training Infrastructure: Create scalable systems for post-training AI models, focusing on orchestration, performance optimization, and monitoring capabilities.Implementing Realistic Model Evaluations: Develop metrics for evaluating AI agent performance and establish the infrastructure and tools necessary for conducting these evaluations.Influencing Technical Strategy: Take charge of architectural decisions, impact product roadmaps, and contribute significantly to our engineering culture as an early-stage team member.About YouYou might be a great fit for this role if you possess the following qualities:Adept at leveraging language models effectively.Ability to innovate and think outside the box.A minimum of 4 years of software engineering experience, showcasing your ability to take ownership of projects.Proficiency in Python, Rust, or TypeScript, with the capability to work across the entire software stack.Hands-on experience with modern deployment practices, containerization, and cloud infrastructure (such as Kubernetes, AWS, or GCP).Strong problem-solving skills demonstrated through algorithmic challenges or complex system design tasks.Nice-to-HavesPreferred candidates will have experience in:Machine learning infrastructure or reinforcement learning.

At Magic, we are committed to creating safe AGI that propels humanity forward in addressing the world’s most pressing challenges. We believe that the key to achieving safe AGI is through the automation of research and code generation, which enhances model performance and ensures alignment more reliably than human efforts alone. Our innovative approach integrates cutting-edge pre-training techniques, domain-specific reinforcement learning, ultra-long context, and advanced inference-time computation.Role OverviewAs a Software Engineer on our RL Research & Environments team, you will be instrumental in designing and managing data systems, evaluation frameworks, and environmental setups that enhance model capabilities following pre-training.This position emphasizes post-training processes, where your responsibilities will include identifying capability gaps, creating specialized datasets, designing reward structures, and executing iterative training cycles that lead to significant improvements in user interactions. You will oversee the infrastructure and experimental workflows that bridge product objectives with measurable capability enhancements.Our long-context models present unique post-training challenges, including long-horizon reasoning, maintaining coherence over extended tasks, optimizing context utilization, and enabling tool-assisted behaviors. You will develop systems that reveal failure modes, produce high-value training data, and facilitate rapid reinforcement learning iterations at scale.This role offers the potential for growth, allowing you to take ownership of key capability domains, delve deeper into RL systems, or influence post-training strategies as we enhance the performance and reliability of long-context models.Key ResponsibilitiesDesign and construct post-training datasets leveraging synthetic generation, targeted data collection, and self-play methodologies.Implement filtering, scoring, and mixture strategies for reinforcement learning and post-training datasets.Develop and sustain evaluation frameworks that identify long-context failure modes.Create reward signals and training environments aimed at specific capability advancements.Conduct ablation studies across various data sources, reward configurations, and long-horizon task designs.Enhance the reliability and observability of post-training data and environment pipelines.Collaborate closely with Product and Research teams to translate capability objectives into quantifiable iteration cycles.Ideal Candidate ProfileSolid foundation in software engineering principles.Proven experience in developing or managing large-scale data or machine learning systems.Strong analytical skills and a passion for tackling complex problems.

At Yutori, we are revolutionizing the way individuals engage with the online world by developing AI agents that can seamlessly manage everyday digital tasks. Our mission is to create a fully integrated agent-first ecosystem, encompassing everything from training proprietary models to designing intuitive generative product interfaces.We invite a passionate and skilled AI Engineer to join our founding team and contribute to our vision of building superhuman AI agents capable of performing actions across the web.Our founders—Devi Parikh, Abhishek Das, and Dhruv Batra—bring decades of expertise in AI research and product development from their tenure at Meta, focusing on generative, multimodal, and embodied AI. Our diverse team blends advanced AI knowledge with innovative product design to execute Yutori's ambitious mission.Supported by an exceptional group of visionary investors—including Elad Gil, Sarah Guo, Jeff Dean, Fei-Fei Li, and others—Yutori is poised for remarkable growth and development.

Preference Model develops reinforcement learning environments that mirror the complexity of real-world tasks. The company focuses on building diverse RL tasks and detailed reward structures, aiming to push the boundaries of artificial intelligence. The founding team brings experience from developing data infrastructure and datasets for Claude at Anthropic, and Preference Model works closely with top AI research labs. Role overview The Senior Software Engineer - Reinforcement Learning Environments position centers on designing and delivering RL environments that challenge and improve current AI models. This role involves leading complex projects, including multi-step workflows and realistic stakeholder interactions, within a large codebase. Engineers work directly with the founders and a small, collaborative team, delivering environments used for training advanced models at partner labs. The position provides significant autonomy, regular feedback, and support for professional development. What you will do Design, build, and iterate on reinforcement learning tasks, taking them from concept through evaluation. Lead the development of sophisticated environments, focusing on complex workflows and coding standards. Interact with coding agents, review their outputs, and identify subtle failures. Analyze whether issues stem from model limitations or environment design, then redesign tasks to reveal deeper failure modes. Contribute to building and maintaining the core infrastructure and tools for the environments team. Mentor junior engineers as the team expands. Location This role is based in San Francisco.

Handshake connects over 20 million professionals, 1,600 educational institutions, and 1 million employers, including every Fortune 50 company, through its career network focused on the AI economy. The platform supports everything from freelance AI training projects to full-time positions and is on a path to triple annual recurring revenue by 2025. The Software Engineer II - Reinforcement Learning Environments role centers on building and evolving Handshake’s RLE platform. This platform enables advanced AI models to learn and tackle real-world challenges. The position is based in San Francisco, CA, and requires in-office work five days a week. What you will do Develop and enhance core components for reinforcement learning environments and their infrastructure. Design and implement backend systems and efficient data pipelines. Translate complex product and research requirements into reliable, working systems. Create modular, reusable workflow domains to streamline processes. Focus on improving system reliability, observability, and performance tracking. Requirements 4–6 years of experience in backend development, distributed systems, or machine learning infrastructure. Strong skills with ReactJS and TypeScript. Experience working with relational databases, especially PostgreSQL, and data modeling. Familiarity with AWS or GCP and CI/CD practices. Ability to manage projects independently from planning through production. Bonus points Background in simulation systems or performance optimization. Why join Handshake? Help shape the future of careers in the AI sector and make a measurable impact. Work directly with top AI labs, Fortune 500 companies, and leading universities. Join a team with alumni from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir. Be part of a business experiencing significant revenue growth.

About AfterQuery AfterQuery develops training data and evaluation frameworks that leading AI labs use to improve their models. The team partners with major research institutions to build datasets and run assessments that go beyond standard benchmarks. As a post-Series A company based in San Francisco, AfterQuery values contributions from every team member. Work here directly shapes the next generation of AI models. Role Overview The Reinforcement Learning Environment Engineer designs datasets and evaluation systems that influence how advanced AI models learn and improve. This role involves close collaboration with research teams, hands-on experimentation with new data collection methods, and the creation of metrics to track model progress. Work moves from theoretical analysis to practical experiments, feeding directly into large-scale model training efforts. What You Will Do Develop data segments that expose key failure modes in sectors such as finance, software engineering, and enterprise operations. Refine reward signals for Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from Value Reinforcement (RLVR) systems. Define quantitative metrics for dataset quality, diversity, and their effects on model alignment and capability. Work closely with research teams to translate training objectives into concrete data requirements and evaluation criteria. This position is based in San Francisco.

Join firecrawl as a Research Engineer specializing in Reinforcement Learning (RL). In this role, you will leverage your expertise to conduct innovative research and develop advanced RL algorithms that push the boundaries of technology. Collaborate with a talented team of engineers and researchers to solve complex problems and contribute to groundbreaking projects.

Be Part of the Future of Autonomous RoboticsAt Bedrock Robotics, we are pioneering the transition of AI from theoretical frameworks to practical applications in the built environment. Our team is comprised of seasoned professionals who have been instrumental in the success of innovative companies such as Waymo, Segment, and Uber Freight. We are at the forefront of deploying autonomous technologies in heavy construction machinery, significantly enhancing the efficiency and safety of multi-billion dollar infrastructure projects across the nation.With backing from $350 million in funding, our mission is to address the urgent need for housing, data centers, and manufacturing facilities, while simultaneously responding to the construction industry's labor shortages.This position is where cutting-edge algorithms meet the practical world of construction. You will work alongside industry experts and top-tier engineers to tackle complex real-world challenges that cannot be simulated. If you are eager to leverage advanced technology for impactful problem-solving within a skilled team, we encourage you to apply.

Join primeintellect as a Research Engineer focused on Reinforcement Learning Infrastructure. In this role, you will be instrumental in advancing our cutting-edge AI technologies. You will collaborate with interdisciplinary teams to develop robust frameworks that enhance machine learning capabilities and drive innovation.As a key player in our engineering team, you will work on designing, implementing, and optimizing systems that support reinforcement learning algorithms. Your contributions will directly impact the efficiency and effectiveness of our AI solutions.

Join Condor Software as a Full-Stack Platform EngineerAt Condor, we are revolutionizing the financial infrastructure that supports clinical development. With billions invested annually in discovering and developing new therapies, we strive to connect clinical operations and finance into a cohesive system. By integrating real-time financial intelligence, we empower R&D and finance leaders with the tools they need to make informed, high-stakes decisions.We are an AI-driven, pharma-native infrastructure provider, scaling industry standards in collaboration with top-tier partners. Our platform facilitates prediction, control, and execution in the most complex R&D environments worldwide.The Importance of Your RoleHaving established ourselves as a trusted partner for enterprise teams, we are now focused on the challenging task of scaling our platform to meet increasing demands. As a rapidly growing company, backed by prominent investors like Felicis and 645 Ventures, this is a unique opportunity to contribute to the foundational infrastructure that will redefine how therapies reach patients.Your ResponsibilitiesAs a Full-Stack Platform Engineer, you will be pivotal in building and scaling the core platform that supports the financial intelligence infrastructure relied upon by leading biopharma companies. This role encompasses critical engineering tasks at the intersection of backend systems, cloud infrastructure, and intelligent automation, with a strong emphasis on reliability and scalability.Your primary focus will be on backend architecture, where you'll design and implement services that drive complex financial and operational workflows. You'll be instrumental in shaping data flow, workflow orchestration, and enabling emerging AI-driven capabilities. This role goes beyond simple integration; you'll be crafting robust primitives that support other teams as our product and customer base expand.Working as a core member of a cross-functional product team, you will closely collaborate with product managers, designers, quality engineers, and data specialists to transition features from concept to production. While backend expertise is crucial, you will also engage across the stack to ensure the platform's capabilities are effectively leveraged.

Join DoorDash as a Senior Deep Reinforcement Learning Engineer and play a pivotal role in revolutionizing the logistics and delivery industry through cutting-edge AI solutions. In this position, you will leverage your expertise in deep reinforcement learning to develop advanced algorithms that optimize our delivery processes and enhance customer experience.

About UsAt Preference Model, we are pioneering the next generation of training data to unlock the full potential of artificial intelligence. While today's models show remarkable capabilities, they often fall short of their potential across diverse applications due to out-of-distribution tasks. We create Reinforcement Learning environments that allow models to tackle real-world research and engineering challenges, iterating and learning through realistic feedback loops.Our founding team comprises seasoned professionals from Anthropic’s data team, where we developed data infrastructure, tokenizers, and datasets for Claude. We collaborate with leading AI laboratories to drive AI closer to its transformative potential and are backed by a16z.About the RoleWe are seeking talented Reinforcement Learning Environments Engineers to design and implement MLE environments. Your primary mission will be to enable Large Language Models (LLMs) to acquire improved reasoning and advanced understanding of modern machine learning concepts. This role is fully remote with a requirement for at least 4 hours of overlap with PST and proficiency in English at a C1/C2 level.

Location: Preference for San Francisco, but remote candidates are welcome to apply.Duration: This internship will last for 10-12 weeks during Summer 2026.Compensation: This is a paid internship opportunity.About UsAt Preference Model, we are pioneering the next era of training data to fuel the advancement of AI technologies. While current models are impressive, they often struggle with diverse applications due to out-of-distribution tasks. Our focus is on developing reinforcement learning (RL) environments where models can engage with complex research and engineering challenges, iterating and learning from realistic feedback mechanisms.Our founding team boasts extensive experience from Anthropic's data division, where we built data infrastructure, tokenizers, and datasets that powered Claude. We collaborate with top AI labs to accelerate AI's journey toward its transformative potential and are proudly supported by a16z.About the RoleWe are seeking talented PhD students and exceptional undergraduate candidates to join us this summer in developing RL training environments tailored for large language models.What You'll DoDesign and implement RL environments to assess LLM reasoning across various ML, systems, and research problems.Produce clean, production-quality Python code (not just notebooks).Utilize Docker to create reproducible environments and troubleshoot issues as they arise.Translate ML research papers and concepts into actionable training tasks.Who We're Looking ForYou are either an undergraduate or a PhD student in Computer Science, Machine Learning, Mathematics, Physics, or a related discipline. You have a knack for writing real code beyond mere research prototypes and you enjoy reading ML literature in your spare time.Must-Have Qualifications:Proficient in Python programming.Understanding of large language models (LLMs), their strengths, and limitations.Self-motivated and capable of taking feedback to iterate quickly.Preferred Qualifications:Familiarity with transformer architecture and experience with training or inference code.Experience in writing CUDA kernels or engaging in low-level GPU programming.Deep knowledge in a particular research area (demonstrated by publications, public code, or strong coursework).A passion for continuous learning and research in the field of AI.

About ChalkChalk is at the forefront of developing a cutting-edge data platform that revolutionizes machine learning applications. We are committed to dismantling the traditional barriers of complexity, latency, and scalability that have limited ML capabilities. By merging high-performance Rust technology with user-friendly tools, we empower developers and organizations alike. Our platform is trusted by leading companies to combat fraudulent credit card transactions, validate identities, and optimize clean energy utilization. Recently, we secured a $50 million Series A funding round, spearheaded by Felicis.About the RoleWe are actively seeking passionate Full Stack Software Engineers to join our dynamic team. This is a unique opportunity to contribute significantly as an early employee within a rapidly growing startup. You will have the autonomy to tackle complex engineering challenges while taking full ownership of your work.Our ideal candidates are versatile engineers who excel at addressing business challenges across all layers of the software stack. At Chalk, we prioritize building robust data processing systems that perform efficiently at scale, all while focusing on an exceptional developer experience.We require engineers capable of crafting outstanding user experiences across both CLI and web interfaces, alongside a strong understanding of a codebase enriched with concepts from compilers, query planners, and distributed systems.Our team works in the office five days a week, with flexibility for unavoidable conflicts. Please note, this is not a hybrid position.What You Will DoCollaborate directly with Chalk’s co-founders to bring our first iteration to production.Develop applications using TypeScript, Python, Go, and React.Contribute to a codebase that incorporates advanced ideas from various fields.

At Bugcrowd, we are redefining the landscape of cybersecurity. Since our inception in 2012, we have been committed to empowering organizations to regain control and stay ahead of cyber threats. By harnessing the collective creativity and expertise of our clients and an elite network of hackers, we leverage our patented AI-driven Security Knowledge Platform™. Our diverse community of hackers excels in uncovering vulnerabilities, swiftly adapting to the evolving threat landscape, including zero-day exploits. With our innovative CrowdMatch™ technology, we provide scalable, tailored solutions to enhance your security posture. Join us as we usher in a new era of crowdsourced security that outpaces cyber adversaries. For more information, visit www.bugcrowd.com. Headquartered in San Francisco and New Hampshire, Bugcrowd is supported by leading investors including General Catalyst, Rally Ventures, and Costanoa Ventures.Job SummaryThe Bugcrowd Reinforcement Learning and Reasoning Team is dedicated to advancing autonomous cybersecurity through the creation of authentic reinforcement learning environments tailored for foundational model applications. As a Staff Engineer, you will be at the forefront of AI Reinforcement Learning development and implementation. Your primary responsibility will be to design and build the infrastructure and tools that convert real-world vulnerability research into extensive reinforcement learning environments for training state-of-the-art AI systems.In this unique role, you will develop training environments that instruct AI systems on hacking and defending software. Your contributions will directly impact the capabilities of next-generation AI models. Rather than focusing on a single application, you will create the underlying infrastructure that generates thousands of environments for training leading-edge AI technologies.Our team operates at the intersection of AI, security research, and systems engineering, crafting environments that enable models to acquire essential skills such as vulnerability detection, exploitation, and remediation.

About Loop Loop is a pioneering data platform revolutionizing the global supply chain. In an industry plagued by disorganized data trapped in PDFs, emails, and outdated systems, we harness the power of AI to transform this chaos into a reliable 'source of truth' that streamlines payments and audits for Fortune 100 companies. Our mission is to create the financial nervous system of a $100 trillion physical economy, ensuring that freight moves efficiently and carriers are compensated immediately. Supported by industry-leading investors such as Founders Fund, Index Ventures, and 8VC, we are on an aggressive growth trajectory. We seek engineers eager to deploy innovative AI solutions that drive the physical economy forward. About You As a Full-Stack Software Engineer, you will collaborate closely with founders and clients to tackle logistics billing and payment challenges. Your contributions will be vital in shaping the product and delivering features that have a real impact on our customers' experiences. You will encounter and resolve complex technical challenges, benefiting from the support and insights of our team. Your influence will help define Loop's culture and growth trajectory. Responsibilities Collaborate across teams (product, design, sales) to gather requirements and deliver thoughtful solutions. Produce high-quality, extensible code. Participate in code reviews to uphold exceptional engineering standards. Facilitate discussions and document processes to achieve optimal technical designs. Engage with various components, spanning frontend to backend and infrastructure. Utilize AI technologies like Cursor, Codex, and Claude to enhance software development and advocate for the integration of AI tools to boost productivity across the organization. Encourage best practices and disseminate technical knowledge within the team. Contribute to Loop's growth through constructive feedback on our products, processes, and culture. Qualifications Willingness to work in the San Francisco or Chicago office three days per week. At least two years of software engineering experience. Proficiency in TypeScript and familiarity with tools such as GraphQL, Relay, React, and Node.js. Experience in early-stage companies, where you have managed end-to-end product delivery and adapted to various roles.

About Owner.comOwner is revolutionizing the growth strategy for local restaurants through cutting-edge AI technology.Our AI-driven platform consistently enhances SEO, marketing, and online ordering processes, facilitating increased first-party orders. Unlike conventional software solutions that burden small business owners with complex systems, Owner offers a reliable, expert-driven solution.Think of Owner as your dedicated team of engineers and marketers, empowering independent restaurants to compete with large chains.Our VisionWe are committed to helping independent restaurants thrive online. However, our mission extends beyond the culinary industry; many local businesses face similar challenges. Large tech corporations are siphoning their customers and profits, making survival increasingly difficult.Once we perfect our approach for restaurants, we aim to extend our solutions to all types of local businesses.In our envisioned future, millions of local business owners will leverage our technology to excel in the digital era.Read our Series C memo here →Our AchievementsSince our inception in 2020, we have generated tens of millions in revenue and processed over half a billion dollars in online orders. Remarkably, 1 in 5 Americans has used an Owner.com website.We take pride in having assisted over 20,000 restaurant owners, saving them nearly $200 million in fees.Join Our TeamOur team is composed of top-tier talent from leading SMB software companies, including Shopify, HubSpot, DoorDash, ServiceTitan, Rappi, Faire, and Stripe, now numbering in the low hundreds. We anticipate scaling rapidly in 2026 to accommodate our customer growth.Work EnvironmentAs a remote-first organization, Owner is headquartered in San Francisco, with a sales hub in Toronto. While most of our team works remotely across the globe, certain roles may prioritize in-person collaboration. Please refer to the role description and consult your recruiter for specific location details.

About HandshakeHandshake is the premier career network tailored for the AI economy, serving over 20 million knowledge workers, 1,600 educational institutions, and 1 million employers, including all Fortune 50 companies. Our platform is trusted for career discovery, recruitment, and professional development, facilitating opportunities ranging from freelance AI training roles to full-time positions. Our unique value proposition is driving remarkable growth, with an expectation to triple our Annual Recurring Revenue (ARR) by 2025.Why is now the best time to join Handshake?Be a key player in shaping the future of careers within the AI economy, creating tangible impacts for your community.Collaborate closely with leading AI research labs, Fortune 500 partners, and top-tier educational institutions.Join a team enriched by leaders from renowned organizations such as Scale AI, Meta, xAI, Notion, Coinbase, and Palantir.Contribute to building a rapidly growing business projected to generate billions in revenue.About the RoleWe are looking for an experienced Senior Engineering Manager to lead our dynamic Reinforcement Learning Environments (RLE) team.The RLE team creates innovative sandbox environments where cutting-edge AI models can learn comprehensive, end-to-end workflows. These environments replicate real-world professional fields such as software engineering, finance, and legal research, complete with realistic tools, constraints, and feedback mechanisms. Rather than relying on static examples, models engage in practical tasks: navigating multi-step processes, utilizing domain-specific tools, managing uncertainty, and optimizing for real-world results.Researchers leverage these environments and the data produced to train state-of-the-art models using reinforcement learning based on execution—focusing not just on predictions but on task fulfillment, quality, and resilience in complex workflows.As a Senior Engineering Manager, you will define the technical direction and long-term strategy of this vital platform. You will lead a growing team of 8-9 engineers and are expected to manage an Engineering Manager in the near future. This strategic role intersects platform engineering, applied AI infrastructure, research tooling, and human-in-the-loop operational systems.Location: San Francisco, CA | 5 days/week in-office