Software Engineer at Magic | San Francisco

At Magic, we are on a mission to develop safe AGI that propels humanity's progress in addressing the world's most significant challenges. We believe that automating research and code generation is the most promising pathway to achieving safe AGI, enabling us to enhance models and address alignment issues more reliably than humans can achieve alone. Our innovative approach integrates frontier-scale pre-training, domain-specific reinforcement learning, ultra-long context, and inference-time computing to realize our vision.About the RoleAs a Software Engineer at Magic, you will engage in developing core systems and product surfaces that directly influence model capabilities and enhance user experience.This position can align with areas such as Pre-training Data, RL Research & Environments, or Product Development, depending on your background and expertise. Regardless of placement, you will be expected to take full ownership of your work: identifying problems, crafting solutions, deploying to production, and iterating based on real-world results.Working with Magic's long-context models presents unique technical challenges, including large-scale data acquisition, long-horizon post-training loops, and developing product workflows that make complex model behaviors understandable and manageable. You will work closely with these constraints, creating systems that are both technically sound and production-ready.This role has the potential to evolve into a deeper specialization in data systems, post-training capability enhancement, or product engineering leadership based on your strengths and interests.What You'll Work OnDepending on your team assignment, your tasks may include:Developing and scaling large distributed data pipelines for pre-trainingDesigning filtering, mixture, and dataset versioning systemsCreating post-training datasets, evaluation frameworks, and reward pipelinesConducting ablations that translate capability goals into quantifiable improvementsBuilding comprehensive product interfaces that integrate seamlessly with the modelDesigning APIs, backend services, and frontend workflows for AI-first experiencesEnhancing the reliability, observability, and performance of production systemsWhat We’re Looking ForSolid foundation in software engineering principlesHigh ownership and comfort in navigating ambiguous problem domainsProven experience in building scalable production systemsAbility to reason through complex technical challenges

At Magic.dev, we are on a groundbreaking mission to develop safe artificial general intelligence (AGI) that propels humanity forward in tackling the world’s most pressing challenges. We believe that the key to achieving safe AGI lies in automating research and code generation, enabling us to enhance models and resolve alignment issues more effectively than human efforts alone. Our cutting-edge strategy incorporates frontier-scale pre-training, domain-specific reinforcement learning, ultra-long context, and advanced inference-time computation to realize this vision.If you are passionate about contributing to this mission and possess high-energy and creativity, we invite you to explore potential opportunities with us, even if they are not currently listed on our careers page. We welcome exceptional talent with open arms.

Hello! I’m Alex, co-founder of Magic Patterns. We're thrilled to announce an exciting opportunity for a Head of Growth to join our dynamic team. Our product-led growth strategy is thriving, and we’re ready to accelerate our momentum. Currently, we engage on platforms like X, LinkedIn, Reddit, and YouTube, but we envision expanding our outreach significantly.At Magic Patterns, you will play a pivotal role in transforming the software development landscape. Our innovative platform is already empowering thousands of teams to deploy software more rapidly. Our mission is to assist product teams in taking their ideas from inception to production, which has attracted Fortune 500 clients and fostered a passionate community. However, we believe it's always day one, and your contribution is crucial!If you’re passionate about startups, AI, and thrive in a fast-paced environment, we can't wait to collaborate with you!

About MercorMercor sits at the forefront of labor markets and artificial intelligence research, collaborating with premier AI laboratories and enterprises to harness the human intelligence crucial for AI evolution.Our expansive talent network empowers the training of cutting-edge AI models, akin to how educators impart knowledge to students—sharing insights, experiences, and contexts that transcend mere code. Currently, our network comprises over 30,000 experts, generating collective earnings exceeding $2 million daily.At Mercor, we are pioneering a unique category of work where expertise fuels AI progress. Realizing this vision necessitates a bold, fast-paced, and deeply dedicated team. You will collaborate with researchers, operators, and AI firms that are at the vanguard of transforming systems that redefine society.As a profitable Series C company, Mercor is valued at $10 billion and maintains an in-office presence five days a week at our new headquarters in San Francisco.About the RoleIn your capacity as a Research Engineer at Mercor, you will operate at the intersection of engineering and applied AI research. You will play a pivotal role in post-training and Reinforcement Learning from Human Feedback (RLVR), synthetic data generation, and large-scale evaluation workflows essential for advancing frontier language models.Your contributions will help train large language models to adeptly utilize tools, exhibit agentic behavior, and engage in real-world reasoning within production environments. You will be instrumental in shaping rewards, conducting post-training experiments, and constructing scalable systems to enhance model performance. Your responsibilities will also include designing and evaluating datasets, creating scalable data augmentation pipelines, and developing rubrics and evaluators that expand the learning potential of LLMs.

About MagicalMagical is at the forefront of agentic automation, revolutionizing the healthcare landscape with cutting-edge AI technology. Our platform is designed to empower healthcare providers by automating labor-intensive tasks, allowing them to concentrate on what truly matters: patient care.By streamlining processes like claims management, prior authorizations, and eligibility assessments in an industry plagued by administrative hurdles, we're facilitating a transformative shift—one that is both necessary and inevitable.Our AchievementsWe are leading the charge in agentic automation, evidenced by:Significant revenue growth as clients expand their usage into new workflows.Quick proof-of-concept demonstrations within just 7 days—far exceeding industry norms.Reliable, self-healing automation solutions that excel where others falter.Unlike other AI companies, we deliver dependable solutions that yield tangible outcomes. With $41 million raised from renowned investors like Greylock, Coatue, and Lightspeed, our founder Harpaul Sambhi brings a wealth of expertise, having previously sold his startup to LinkedIn.About the RoleAs a Senior Software Engineer, Product on our Builder Experience team, you will harness your full-stack expertise to develop features that enable teams to create, configure, and deploy AI agents seamlessly. You will oversee the entire product interface—from user-friendly no-code tools for agent setup to dynamic dashboards for real-time monitoring and assessment.This position is crafted for engineers passionate about creating exceptional user experiences, understanding that stellar UX is crucial for making advanced technology accessible. Collaborating closely with customers and our design team, you'll deliver features that enhance agent development, all while maintaining a firm grasp of the underlying systems to create effective abstractions.This is a hybrid role, requiring you to be in our San Francisco office three days a week.

About MagicalMagical is a cutting-edge automation platform that integrates advanced AI technology into the healthcare sector, providing AI agents that deliver tangible results in production environments.Our mission is to create AI-driven "employees" that streamline tedious, time-intensive workflows that hinder team productivity. We focus on the healthcare industry—a $4 trillion sector entangled in administrative challenges—by automating processes like claims processing, prior authorizations, and eligibility checks, allowing healthcare providers to dedicate more time to patient care.Our AchievementsThe move towards agentic automation in healthcare is on the horizon, and we are at the forefront:Significant revenue growth as clients expand into new workflows prior to renewalRapid 7-day proof-of-concept implementations that showcase real value, unlike the typical months-long processes in the industrySelf-healing automations that are reliable and scalable in production environments, a feat where many competitors struggleUnlike many AI companies that make grand claims, we deliver dependable solutions that yield measurable outcomes. Our funding partners include Greylock, Coatue, and Lightspeed, with a total of $41M raised. Our founder, Harpaul Sambhi, has previously achieved success by selling his first company to LinkedIn.About the RoleAs the Engineering Manager for our Autonomous team, you will lead and grow a talented group of engineers committed to shaping the future of AI agent development, continually pushing the limits of AI and backend system capabilities.Your passion for management will shine as you nurture the professional growth of your engineers. You possess the technical expertise necessary to engage in intricate architectural discussions and translate complex technical hurdles into clear business strategies. In this position, you will be a vital link between our product vision and technical implementation.This role offers a hybrid work environment, requiring 2 days a week in our San Francisco office.

HUD builds infrastructure for generating and evaluating reinforcement learning (RL) training data for advanced AI agents. The team is also developing a marketplace to connect leading labs with high-quality training data. HUD's platform serves frontier labs, Fortune 500 companies, and startups. The company is backed by $15M in funding from top venture capital firms and is part of Y Combinator's W25 cohort. Role overview HUD is seeking Research Engineers in San Francisco to help strengthen quality assurance for training data produced by partner organizations. This position centers on building systems that maintain and improve data quality as demand increases. What you will do Set and uphold quality standards for training datasets. Develop tools and workflows for auditing datasets from suppliers, including sampling methods, validation pipelines (using rules and models), and feedback systems. Assess and refine human-in-the-loop review processes to support quality assurance. Collaborate with data vendors to resolve quality issues, share insights, and encourage better data generation practices. Integrate QA findings into internal tools and the data vendor portal to reduce anomalies, inconsistencies, and edge cases. Requirements Strong skills in Python, Docker, and Linux environments. Experience working with large datasets. Ability to learn quickly and adapt in technical contexts, such as programming competitions. Background in early-stage tech startups and ability to work independently. Familiarity with modern AI tools and large language models (LLMs). Clear communication skills for collaborating remotely across time zones. Preferred qualifications Understanding of common issues in training data. Background in building data validation pipelines or human-in-the-loop review systems. Strong attention to detail, with the ability to identify subtle data inconsistencies or edge cases. Experience designing metrics, experiments, and QA processes, not just executing them.

At magic.dev, we are committed to advancing humanity by developing safe artificial general intelligence (AGI) that tackles the world's most pressing challenges. Our unique approach focuses on automating research and code generation to enhance model performance and alignment more effectively than traditional methods. By leveraging cutting-edge pre-training, domain-specific reinforcement learning, ultra-long context processing, and efficient inference-time computation, we aim to redefine the capabilities of AGI.Role OverviewAs a Research Engineer, you will play a pivotal role in training, evaluating, and deploying large-scale AI models alongside innovative inference-time computing methods. You will contribute to the creation of extensive internet-scale datasets and support the prototyping of groundbreaking research and product initiatives.Key ResponsibilitiesEnhance inference throughput for cutting-edge model architecturesDevelop and refine frameworks that underpin our research and production processesTrain trillion-parameter models using large GPU clustersCurate post-training datasets to bolster specific capabilitiesConstruct internet-scale data pipelines and web crawlersDesign, prototype, and optimize innovative model architecturesContribute to cutting-edge research in long-context, inference-time computation, reinforcement learning, and additional domainsQualificationsProven software engineering expertiseIn-depth understanding of deep learning literatureExperience with both pre-training and post-training of large language models (LLMs)Strong capability to generate and assess research ideasFamiliarity with large distributed systemsProficient in managing substantial ETL workloadsCompensation and BenefitsAnnual salary ranging from $225,000 to $550,000 based on experienceEquity is a significant component of total compensation401(k) plan with a 6% salary matchComprehensive health, dental, and vision insurance for you and your dependentsUnlimited paid time offVisa sponsorship and relocation assistance availableBe part of a small, dynamic, and focused team

About Resolve AIAt Resolve AI, we are redefining the role of software maintenance and production troubleshooting by creating a revolutionary, fully autonomous AI Production Engineer. Our technology is designed to diagnose and resolve intricate system issues from start to finish.Founded by industry leaders Spiros Xanthos and Mayank Agarwal, who are the masterminds behind OpenTelemetry and have previously spearheaded initiatives at Splunk Observability, our team boasts two successful exits to Splunk and VMware.Having successfully secured over $150M in funding from prestigious investors like Lightspeed, Greylock, and Unusual Ventures, alongside notable individuals such as Jeff Dean (Chief Scientist, Google DeepMind) and Fei-Fei Li (Professor, Stanford), we are well-positioned for growth.Joining Resolve AI now presents a unique opportunity to be part of an AI-driven company that is at the forefront of transforming engineering workflows.

The Center for AI Safety (CAIS) is at the forefront of research and advocacy dedicated to addressing the societal-scale challenges posed by artificial intelligence. Our mission is to mitigate the risks associated with AI through innovative technical research, initiatives to foster the field, and strategic policy engagement. Together with our sister organization, the Center for AI Safety Action Fund, we tackle some of the most pressing issues in AI today. In the role of Senior Research Engineer, you will immerse yourself in the dynamic intersection of pioneering machine learning research and dependable engineering practices. You will own research projects from inception to publication, working autonomously with guidance from an advisor. Your responsibilities include designing and conducting experiments on large language models, developing the necessary tools for large-scale model training and evaluation, and transforming findings into research publications. You will collaborate closely with CAIS researchers, as well as external academic and commercial partners, utilizing our compute cluster for extensive training and evaluation. Your work will cover critical areas such as AI honesty, robustness, transparency, and the investigation of trojan/backdoor behaviors, all aimed at reducing the real-world risks posed by advanced AI systems.

About Liquid LabsAt Liquid AI, research has always been at the forefront of our mission. Liquid Labs serves as a dedicated internal research accelerator, facilitating groundbreaking advancements in the development of intelligent, personalized, and adaptive machines.Our roots extend back to MIT CSAIL, where pioneering work on Liquid Neural Networks established a new category of efficient sequence-processing architectures. This research laid the groundwork for our Liquid Foundation Models (LFMs), which are scalable, multimodal models designed for real-world applications in resource-constrained settings.In Liquid Labs, we continue this legacy by advancing the realm of efficient, adaptive intelligence through both fundamental research and practical engineering efforts.We collaborate closely with Liquid’s core foundation model and systems teams to turn theoretical concepts into deployable capabilities, setting the stage for a new era of powerful and efficient intelligent systems.About The Role:As a Research Engineer at Liquid Labs, you will be part of a dynamic, high-impact team pushing the boundaries of adaptive intelligence. You will be responsible for designing and implementing innovative architectures, training methodologies, and inference strategies to expand the potential of efficient AI.Your work will blend research and engineering, as you translate scientific concepts into functional systems, publish findings that advance the field, and deploy solutions that redefine what is achievable.While we prefer candidates from San Francisco and Boston, we welcome applications from other locations within the United States.

Join the Center for AI Safety (CAIS), a premier research and advocacy institution dedicated to minimizing large-scale societal risks associated with artificial intelligence. We tackle the most pressing challenges in AI through innovative technical research, community-building initiatives, and active policy engagement, alongside our sister organization, the Center for AI Safety Action Fund.As a Research Engineer, you will operate at the forefront of advanced machine learning research and dependable engineering practices. Your role will involve designing and executing experiments on large language models, developing the necessary tools for extensive model training and evaluation, and translating findings into publishable research. You will work collaboratively with CAIS researchers and external academic and commercial partners, utilizing our compute cluster to conduct large-scale training and evaluations. Your work will focus on critical areas such as AI honesty, robustness, transparency, and the identification of trojan/backdoor behaviors, all aimed at mitigating real-world risks posed by sophisticated AI systems.

At Magical, we are transforming the way work is accomplished.Our cutting-edge AI platform introduces "AI employees" to the workplace, tackling monotonous and draining tasks that hinder team efficiency. This empowers organizations to operate more swiftly and effectively, ultimately enhancing outcomes in critical areas such as patient care.As we spearhead the shift towards agentic work, we are rapidly scaling our product from $0 to $XM ARR in just a few months. We are seeking innovative engineers to help us achieve $XXM ARR. Joining our founding team means you will not only be coding but also influencing the future of work with a small, driven team at the forefront of AI advancements.Supported by prominent investors such as those behind OpenAI, Anthropic, Huggingface, and Notion, including Greylock, Coatue, and Lightspeed, we have a robust runway and a vast market waiting to be explored.

Job DescriptionEmbrace the future of competitive advantage with Eragon, where we create bespoke AI systems that are meticulously tailored to understand your unique business landscape.At Eragon, we focus on developing AI models that leverage proprietary data, deployed directly within customer environments and continuously refined through real-world interactions. Our models not only respond but evolve, improving with each user engagement.We utilize a cutting-edge reinforcement learning framework known as RLQF (Reinforcement Learning from Query Feedback) that transforms user interactions into valuable training signals, establishing a cycle of ongoing enhancement that surpasses traditional fine-tuning methods.The RoleAs an Applied Research Engineer, you will be responsible for designing, training, and deploying advanced models that drive real business operations.This position is not about theoretical research; you will engage directly with customer data, constraints, and feedback, crafting solutions that excel in production settings. You will manage the entire lifecycle of the project, from defining the problem and designing data structures to training, evaluating, and iterating based on live performance.What You’ll DoTrain and adapt models: Fine-tune and post-train models on customer-specific data utilizing RLQF among other techniques.Close the loop: Convert real user interactions, corrections, and workflows into actionable training signals.Own end-to-end systems: Oversee the process from data ingestion and curation through to training, evaluation, and deployment.Evaluate in production: Create evaluation frameworks that accurately reflect real-world performance, rather than relying solely on benchmarks.Work with customers: Collaborate closely with users to comprehend their workflows and translate these into model functionalities.Ship and iterate: Focus on the continuous improvement of models based on live feedback and measurable outcomes.What We’re Looking ForExtensive hands-on experience in training, fine-tuning, or post-training machine learning models.Proficiency in handling messy, real-world data as opposed to only clean benchmarks.Familiarity with reinforcement learning techniques, feedback-driven training such as RLHF or RLAIF, and evaluation systems.Adeptness at quickly transitioning from problem identification to data management, model development, and iterative improvement.Strong engineering instincts with a comfort level in managing systems end-to-end.A proactive approach to shipping and enhancing systems, rather than solely focusing on research.

About CartesiaAt Cartesia, our vision is to create the future of artificial intelligence—intelligent systems that are seamlessly integrated into daily life. We aim to overcome current limitations by enabling models to continuously understand and analyze vast streams of audio, video, and text data—ranging from 1 billion text tokens to 1 trillion video tokens—right on your device.Our pioneering team, comprised of PhDs from the Stanford AI Lab, has developed State Space Models (SSMs), a groundbreaking approach to training efficient, large-scale foundation models. With a rich blend of expertise in model innovation and systems engineering, alongside a product-focused engineering team, we are committed to developing and delivering cutting-edge AI models and user experiences.Supported by prominent investors including Index Ventures and Lightspeed Venture Partners, as well as many esteemed advisors and over 90 angel investors from diverse industries, we are at the forefront of AI advancements.About The RoleIn our quest to create truly global AI, we must train our models using datasets that represent the vast diversity of languages and cultures around the world. We are looking for a Research Engineer to take charge of the quality and comprehensiveness of the data that drives our models. As our in-house expert in global data, you will ensure that our models excel across multiple languages, leveraging your keen understanding of linguistic subtleties and your enthusiasm for building inclusive, large-scale datasets.Your ImpactDesign and construct extensive datasets for model training, conducting controlled experiments to evaluate their effect on model performance.Develop assessments for speech models through both manual annotation and automated evaluation metrics.Utilize data generation techniques to enhance model intelligence and reduce biases.Create automated quality control systems to validate and filter the generated data.Collaborate with product teams to ensure optimal support for key languages and markets.What You BringProven experience in developing or working with extensive multilingual datasets.Familiarity with generative models, including speech, text, or multimodal systems.Ability to guide human annotation and evaluation across various languages.Strong analytical skills and a passion for data-driven decision-making.

Join the Center for AI Safety (CAIS), a pioneering research and advocacy organization dedicated to addressing the societal-scale risks posed by artificial intelligence. We tackle the most pressing challenges in AI through rigorous technical research, innovative field-building initiatives, and proactive policy engagement, in collaboration with our sister organization, the Center for AI Safety Action Fund.As a Research Scientist, you will spearhead and conduct transformative research aimed at enhancing the safety and dependability of cutting-edge AI systems. Your responsibilities will include designing and executing experiments on large language models, developing the necessary tools for training and evaluating models at scale, and converting your findings into publishable research. You will work closely with CAIS researchers and external partners from academia and industry, utilizing our compute cluster for large-scale model training and evaluation. Your research will focus on critical areas such as AI honesty, robustness, transparency, and the detection of trojan/backdoor behaviors, all aimed at mitigating real-world risks associated with advanced AI technologies.

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We aspire to create a future where everyone can access the knowledge and tools necessary to harness AI for their individual needs and aspirations.Our team consists of scientists, engineers, and innovators who have developed some of the most renowned AI products, including ChatGPT and Character.ai, as well as open-weight models such as Mistral. We are also contributors to popular open-source initiatives like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking talented engineers to develop the libraries and tools that will expedite research at Thinking Machines. You will take charge of our internal infrastructure, which includes evaluation libraries, reinforcement learning training libraries, and experiment tracking platforms, all aimed at enhancing research velocity over time.This position emphasizes collaboration; you will engage directly with researchers to pinpoint bottlenecks and challenges. Your success will be measured by the trust researchers place in your systems and their enjoyment of using them.What You'll DoDesign, develop, and manage research infrastructure, including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.Create high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessments.Establish systems for reproducibility, traceability, and stringent quality control throughout research experiments and model training processes. Implement monitoring and observability.Collaborate closely with researchers to identify obstacles and unlock new capabilities. Manage research tools like a product manager, actively seeking feedback and tracking user adoption.Work alongside infrastructure, data, and product teams to ensure seamless integration of tools across the technical stack.

Join xdof at a pivotal moment as we lead the charge in the development of general-purpose robotics. With frontier labs racing to create advanced robotic systems, high-quality training data is a critical challenge. Our mission is to build the essential infrastructure that supports foundational models – from data collection systems and operational capabilities to an exabyte-scale data warehouse and innovative software toolchains. This will empower our partners to advance the field of robotics.As a Research Engineer, you will be at the forefront of designing, constructing, and deploying real-world robotic learning systems. Your work will encompass manipulation, locomotion, and control, transitioning robots from raw hardware to fully operational systems.This hands-on position requires you to take ownership of systems from inception to deployment on actual robots. You will play a crucial role in establishing the technical foundations that facilitate large-scale robotic learning.

Be Your Own LabAt Prime Intellect, we are dedicated to constructing the foundational infrastructure that leading AI laboratories utilize internally, making it accessible to all. Our advanced platform, Lab, integrates environments, evaluations, sandboxes, and high-performance training into a cohesive full-stack system for post-training at the forefront of AI development. From Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT) to tool utilization and agent workflows, we ensure every aspect is validated through our own rigorous testing, training cutting-edge models on the same robust stack we offer to our users. We seek individuals who are passionate about contributing at the intersection of pioneering research and tangible infrastructure.Recently, we secured $15 million in funding (with a total of $20 million raised) led by Founders Fund, along with contributions from Menlo Ventures and esteemed investors such as Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI), and many others.About the RoleWe are in search of a Forward-Deployed Research Engineer (FDRE) who will act as the key technical liaison between Prime Intellect and our most valued clients: AI companies, research institutions, and enterprises implementing post-training and agentic RL on our platform.This role transcends traditional research; you will primarily engage directly with customers to gain insights into their models, workflows, and objectives. Your responsibility will be to convert these insights into actionable training runs, environment designs, evaluation harnesses, and deployment strategies using the Lab stack. You will be the catalyst for making our platform operate effectively for real-world applications.Collaboration with our research, product, and infrastructure teams will be essential, as you will provide valuable field insights to inform future developments, ensuring we align our offerings with actual customer needs.What You'll DoCustomer Engagement & Technical DeliveryWork directly with key customers to comprehend their agent architectures, identify failure modes, and clarify product goalsCreate and develop tailored RL environments, evaluation tools, and verification methods that define success for each specific domainDesign agent scaffolding — including tool usage, multi-step reasoning, memory functions, and sandbox execution — customized to match client workflowsSet up and initiate training sessions on Lab, refining reward functions, rollout strategies, and evaluation standardsLead technical engagements from inception to deployment, ensuring seamless integration and functionality.

Join Convex and revolutionize application development!At Convex, we are dedicated to transforming the way developers construct applications. Our mission is to fundamentally reshape software development on the Internet by enabling developers to create fast, reliable, and dynamic applications without the need for backend teams. Our comprehensive full-stack app platform is meticulously designed with database, compute, and backend abstractions, allowing developers and LLMs to innovate quickly while ensuring that products remain scalable and manageable over time.About Our Team:Our team comprises engineers who have designed and built some of the most significant backends worldwide, handling exabytes of data and millions of transactions per second, while delivering desktop and mobile software to billions of devices. We are a friendly, collaborative, and passionate group that thrives on working together in our San Francisco office.Your Role:At Convex, we have a vast product surface area that includes our dashboard, insights, third-party integrations, project management, billing systems, email services, logging, and streaming, all of which are crucial to our customers’ businesses. These products need to be robust, reliable, intuitive, and enjoyable to use. We seek engineers who are passionate about creating excellent product experiences and expanding our offerings, particularly as we advance into higher markets.If you are an engineer with a keen design sensibility who values quality and is skilled at prioritization while collaborating directly with customers and business teams, you will likely be an excellent fit for our team. This role is for those who aspire to do impactful work and utilize product insights to determine the “why” and “what” of our development process, not just the “how”.Your Responsibilities Include:Designing, building, and maintaining Convex’s platforms, dashboard UI, integrations, billing, and other services.Collaborating directly with customers and leadership to define feature development plans.Developing a deep understanding of customer needs and business objectives to make informed tradeoffs and prioritize effectively.Establishing best practices and reliability standards as our team and systems grow.Contributing to a culture of excellence in product development.