Cleanlab's mission is to democratize safe, trustworthy, and reliable AI. We believe the most promising path to such AI follows a data-centric approach. We build open-source algorithms and no-code SaaS solutions based on our research to help individuals and teams systematically and algorithmically curate high-quality datasets, which enables AI systems to train reliably on real-world messy and error-prone data.
The roleAs an infrastructure engineer at Cleanlab, you will be responsible for building the infrastructure that supports the SaaS and VPC deployments of Cleanlab Studio, a user-friendly web app powered by our data-centric ML algorithms.
What you might work onDesigning, implementing, and operating infrastructure for web apps, data ingestion, data processing, and ML model trainingOptimizing infrastructure for reliability, security, scalability, and costIdeating and prototyping novel product features enabled by our researchCollaborating with our data scientists and machine learning engineers to integrate feedbackOur tech stack includes Python, AWS, CDK, Docker, and Kubernetes.
What we're looking forTrack record of implementing and maintaining high-quality infrastructure for challenging projectsExceptional programming skillsAbility to thrive in a fast-paced environment, navigate ambiguity, and adapt to changing prioritiesCleanlab strives to be a place where high-potential individuals come together to build something great. We value grit and the ability to learn quickly as much as skill and experience.
Our software engineering jobs have no hard requirements (like years of industry work experience, or even experience with the programming languages we use). Instead, we evaluate each application individually and consider any aspects that might stand out.
If you're a great hacker, you believe in our mission, and you identify with our culture, we encourage you to apply!
Working at Cleanlab is awesome! Beyond the opportunity to work at a well-funded AI startup with an incredible, friendly founding team of MIT graduates, all full-time employees receive the following:
Travel enhances our empathy with different cultures and enables us to work together more effectively. It's how we grow and learn: traveling is an essential part of what makes us human. At Cleanlab, every two months you will receive a reimbursable travel benefit.Premium health insurance (+ dental and vision)Professional development stipend to keep up with the latest innovations in ML and software.Competitive salary (+ equity offering for certain roles), with regular opportunities for a raise if things are going well.Relocation bonus to move to San FranciscoThe compensation range for this role is $160,000 to $230,000. The final offer details are determined by several factors including candidate experience/expertise and may vary from the pay range provided.
About usPrior to Cleanlab, our founders (3 ML PhDs from MIT) worked at OpenAI, Google, Microsoft, Amazon, AWS, Facebook AI Research (FAIR), Dropbox, Oculus, Palantir, NASA, General Electric, MIT Lincoln Laboratory, MIT, Harvard, and Stanford – at every place we worked we repeatedly encountered the same issue – AI solutions failed to work reliably on real-world, human-centric data due to label errors and poor data quality.
Everything we do at Cleanlab is guided by our north star – to improve the world's ML data more easily and quicker than any other solution – enabling AI systems to train more reliably on real-world, messy, error-prone data. We develop next-generation data-centric AI, open-source algorithms and provide no-code SaaS enterprise solutions to help individuals and teams at companies (across all industries) diagnose/fix issues in their datasets and produce more reliable ML models by providing clean labels for training.
While many companies can help store/manage data or develop ML models, there exist few solutions today to improve the quality of existing data, which is the core asset of the modern enterprise. This is where you come in. At Cleanlab, you'll be able to take ownership of critical projects that pioneer the future of data-centric AI.
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