Data Curation Associate Jobs

At Lyft, we are building a more connected world, ride by ride. Whether it's an everyday commute or a journey that changes everything, we are driven by our purpose: to serve and connect. To create the best ride for all, we start in our own community by creating an open, inclusive, and diverse organization where all team members are recognized for their input. Data Curation Operations is a team which supports all ongoing curation projects for Mapping and other teams at Lyft. Our mission is to improve customer experience in Lyft products by providing high-quality, comprehensive, and efficient data curation operations. As a Data Curation Associate at Lyft, you will play a crucial role in ensuring the accuracy, quality, and usability of Map Data. You will work closely with various teams to curate and maintain data sets that are essential for our operations and decision-making processes. We are looking for someone who has good analytical skills and is able to work with a large amount of information. If you have an entrepreneurial spirit, like to solve challenging problems, and want to be part of the next big thing at Lyft, we would love to meet you! Responsibilities: Curate data sets, ensuring accuracy, consistency, and completeness. Document data and tooling issues meticulously for analysis. Create and maintain comprehensive project documentation. Accountability: Perform assigned tasks according to allocation, taking responsibility for assigned workload, expectations a

Spotify is looking for a creative, strategic, and sharp editorial voice to join the US Podcast Editorial team. In this role, you'll lead high-visibility editorial projects end to end, partner cross-functionally as a creative and content expert, and shape how listeners discover and engage with podcasts and emerging formats on Spotify. The ideal candidate is a strong writer with a distinct editorial point of view who operates with independence and thrives at the intersection of creative judgment and platform strategy. This role reports into the Associate Director, Podcast Editorial & Audience Development. What You'll Do Drive and lead strategic editorial projects from brief through execution and results, including tentpole programming, format testing, and seasonal initiatives Write compelling editorial copy and commentary that brings Spotify's editorial voice to life across curated surfaces and promotional moments Partner cross-functionally as a creative and content expert across workstreams spanning product, personalization, and marketing Own curation of high-complexity or high-visibility editorial surfaces, maintaining quality, cultural relevance, and freshness Collaborate with Partnerships to develop strategies that grow creator audiences on platform Represent Editorial's perspective in cross-functional working groups and planning conversations Identify cultural moments, content trends, and audience opportunities tha

About Karya: Why was Karya on the cover of the Time Magazine , highlighted by Satya Nadella , and invited to present its work to Sundar Pichai one on one? In part, because Karya is on a mission to provide AI enabled earning and learning opportunities to communities with high talent, but low access to opportunities. Karya achieves this while also delivering high quality, timely, and price competitive data to its clients. Karya builds high quality datasets for large companies like Google and Microsoft, while providing ethical work opportunities and fair wages to its workforce. Karya’s workers make nearly 20 times the Indian minimum wage and through our one-of-a-kind digital work platform, we have delivered over 40 million digital tasks and have positively impacted over 100 thousand workers. In the coming years, our goal is to rapidly scale our impact by bringing economic opportunities to millions of underserved users in India. With a rapidly growing global presence, we are also looking to expand our client base in the Indian market by partnering with leading Indian enterprises. About the Role We are looking for a detail-oriented and curious Data Curation Intern to help build high-quality datasets for training AI/ML models with a specific focus on Indian language and multilingual data. You will work with large open-source datasets (e.g., Sangraha by AI4Bharat) that require significant cleaning, structuring, and enrichment before they can be used effectively in model traini

NVIDIA is a global leader in high-speed computer vision, artificial intelligence (AI), and deep learning. Our team develops data engineering solutions that empower AI developers in autonomous vehicle (AV) domains to innovate quickly and effectively at scale. Are you ready to take on a senior technical role in building high-performance AI data pipelines? We seek an exceptional individual to design and optimize microservices and data pipelines to process massive volumes of AV data and enable seamless data mining and AI training. The ideal candidate will bring expertise in big data processing and distributed computing to create efficient solutions and overarching architectures for challenges such as video data curation, behavioral search, and AI dataset management. What you'll be doing: Scope and build tools, microservices, workflows, and distributed applications to accelerate data mining and AI training. Design and implement solutions for streaming, resilience, logging, security, authentication, workflow orchestration, and data management. Deploy AI models. Design and develop Retrieval-Augmented Generation (RAG) workflows enabling hybrid and agentic patterns. Analyze and operationalize complex distributed systems for speed-of-light performance. What we need to see: Experience developing high-performance, scalable software systems. <

The Ontology Engineer is a foundational technical hire on the AI, ML and Data Science team specializing in Knowledge Representation. This role is responsible for designing and maintaining the formal ontological architecture that makes cross-organizational data alignment. This is not a taxonomy or metadata management role. It requires genuine formal depth in description logics, upper ontology theory, and the ability to reason about what an ontology commits to and what it leaves open. Our platform sits between hospitals, distributors, GPOs, manufacturers, and regulators, enabling transactional execution, clinical data alignment, and analytics optimization across organizational boundaries. Each party maintains its own implicit ontology encoded in its schemas, workflows, and data. The Ontology Engineer will define the formal structures and processes that make alignment across them possible. These structures should be auditable, compositionally sound, and maintainable over a multi-year lifecycle as all parties' systems evolve. This Engineer will work directly with teammates that are familiar with ontology formalisms and with domain experts who understand the operational realities of HCSC data. They will be expected to make and defend design decisions at the level of formal correctness, not just practical convenience, and to direct and evaluate LLM-assisted ontology discovery and enrichment pipelines with the rigor that formal alignment demands. Essential Duties:

Why RoboForce RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are looking for a Senior / Staff AI Research Engineer, Data Infrastructure to build the data and learning engine behind RoboForce's Physical AI stack. In this role, you will own the full pipeline — from raw teleoperation and UMI device data collection through curation, annotation, and storage, to post-training infrastructure that scores demonstrations, identifies failure patterns, and closes the loop back into model retraining. Responsibilities Design and maintain end-to-end data collection pipelines ingesting multimodal demonstration data from teleoperation devices and UMI hardware, including synchronization, versioning, and distributed storage at scale. Build annotation tooling and data curation workflows — quality filtering, deduplication, episode scoring, and domain reweighting — to produce high-quality training datasets for robot policy learning. Develop post-SFT reinforcement learning infrastructure: implement reward scoring on demonstrations, mine and categorize failure patterns, and feed curated failure data back into the retraining loop. Build evaluation and test infrastructure to log policy rollouts on-robot, capture structured results, and surface actionable diagnostics for the research team. C

About Prodigal Prodigal is the connected AI platform leading financial institutions use to run their operations. We work with banks, lenders, credit unions, and other financial companies that lend money to people and manage those relationships over time. These institutions make millions of high-stakes decisions every day. Who should they reach? When should they reach them? What should they say or offer? When should a case move to a human? How should that change based on the borrower, the account, previous interactions, and the regulations involved? Getting those decisions right requires a deep understanding of the people, processes, rules, and edge cases behind them. Prodigal has spent the last eight years building that understanding. More than a billion interactions between financial institutions and their customers have shaped the intelligence, guardrails, and AI agents we now run in production across North America. Today, our AI agents analyze conversations, capture context, guide human agents, decide the next action, conduct customer conversations, orchestrate outreach, and help people complete payments and resolutions. They are connected, so what is learned in one interaction can inform what happens next. We are expanding this swarm of AI agents across more of the work financial institutions do: originations, document processing, back-office workflows, servicing, and other critical operations where money, identity, people, and regulation intersect. We are backed by Y Com

About the Role At Sigma, we’re not just adding AI—we’re building the future of how people work with data. Our platform already lets users explore billions of rows of data in seconds with a spreadsheet-like interface, analyze and present their data in workbooks, and build data apps and workflows. Now we’re pushing further, applying AI to reshape how people build in Sigma, discover insights, and make smarter decisions—fast. That’s where you come in. As an AI/ML Engineer, you’ll join a growing team focused on building the AI foundation that will power Sigma for the future. Your work will become an integral part of the workflow for the thousands of enterprises that run on Sigma. What You’ll Do Partner with product, design, and engineering teams to identify high-impact AI/ML opportunities Prototype and productionize AI systems that feel intuitive but do a lot under the hood—recommendations, natural language interfaces, agentic workflows, and more Develop and scale AI/ML infrastructure that powers both internal tooling and customer-facing features Tackle novel UX problems at the intersection of AI, BI, and apps What You Bring Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (required) 10+ years of experience building and deploying production-grade AI/ML systems Deep knowledge of machine learning, deep learning, and applied AI Experience across the full ML lifecycle: data curation, training, deployment, monitoring A track record

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities. In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models. You will: Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities. Design and experiment new approaches to preference optimization. Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning. Excellent written an

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE: As a multimodal engineer on the Imagine Model Team, you will develop cutting-edge AI experiences beyond text, with a strong focus on enabling high-fidelity understanding and generation across image and video modalities, while also incorporating audio where it enhances visual content (e.g., synchronized audio for video). Responsibilities span data curation, modeling, training, inference serving, and product integration, covering both pretraining and post-training phases. You will collaborate closely with product teams to push model frontiers and deliver exceptional end-to-end user experiences. RESPONSIBILITIES: Create and drive engineering agendas to advance multimodal capa

Scale's LLM post-training platform team builds our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework. Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation. Research and integrate state-of-the-art technologies to optimize our ML system. Ideally you’d have: Passionate about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc. Strong software engineering

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation Research and integrate state-of-the-art technologies to optimize our ML system Ideally you’d have: Strong excitement about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. Strong written and verbal communicati

About the Role At Sigma, we’re not just adding AI—we’re building the future of how people work with data. Our platform already lets users explore billions of rows of data in seconds with a spreadsheet-like interface, analyze and present their data in workbooks, and build data apps and workflows. Now we’re pushing further, applying AI to reshape how people build in Sigma, discover insights, and make smarter decisions—fast. You can learn more about our vision for AI/ML at Sigma in our blog post . That’s where you come in. As an AI/ML PhD Intern , you’ll join a growing team focused on building the AI foundation that will power Sigma for the future. Your work will become an integral part of the workflow for the thousands of enterprises that run on Sigma. We're looking for PhD students that are passionate about the intersection of AI, business, and data. What You’ll Do Conduct original AI/ML research in search, recommendations, natural language processing, agentic workflows and more Prototype and productionize AI systems that feel intuitive but do a lot under the hood Tackle novel UX problems at the intersection of AI, analytics, and data apps Contribute to the development of scalable AI/ML infrastructure for experimentation, training, and evaluation of large-scale models. What You Bring Current PhD student studying AI/ML at a U.S. accredited university Deep understanding of machine learning, including deep learning algorithms, model architectures and optimizat

About the Role At Sigma, we’re not just adding AI—we’re building the future of how people work with data. Our platform already lets users explore billions of rows of data in seconds with a spreadsheet-like interface, analyze and present their data in workbooks, and build data apps and workflows. Now we’re pushing further, applying AI to reshape how people build in Sigma, discover insights, and make smarter decisions—fast. You can learn more about our vision for AI/ML at Sigma in our blog post . That’s where you come in. As an AI/ML PhD Intern , you’ll join a growing team focused on building the AI foundation that will power Sigma for the future. Your work will become an integral part of the workflow for the thousands of enterprises that run on Sigma. We're looking for PhD students that are passionate about the intersection of AI, business, and data. What You’ll Do Conduct original AI/ML research in search, recommendations, natural language processing, agentic workflows and more Prototype and productionize AI systems that feel intuitive but do a lot under the hood Tackle novel UX problems at the intersection of AI, analytics, and data apps Contribute to the development of scalable AI/ML infrastructure for experimentation, training, and evaluation of large-scale models. What You Bring Current PhD student studying AI/ML at a U.S. accredited university Deep understanding of machine learning, including deep learning algorithms, model architectures and optimizat

About the Role At Sigma, we’re not just adding AI—we’re building the future of how people work with data. Our platform already lets users explore billions of rows of data in seconds with a spreadsheet-like interface, analyze and present their data in workbooks, and build data apps and workflows. Now we’re pushing further, applying AI to reshape how people build in Sigma, discover insights, and make smarter decisions—fast. That’s where you come in. As an AI/ML Engineer, you’ll join a growing team focused on building the AI foundation that will power Sigma for the future. Your work will become an integral part of the workflow for the thousands of enterprises that run on Sigma. What You’ll Do Partner with product, design, and engineering teams to identify high-impact AI/ML opportunities Prototype and productionize AI systems that feel intuitive but do a lot under the hood—recommendations, natural language interfaces, agentic workflows, and more Develop and scale AI/ML infrastructure that powers both internal tooling and customer-facing features Tackle novel UX problems at the intersection of AI, BI, and apps What You Bring Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (required) 10+ years of experience building and deploying production-grade AI/ML systems Deep knowledge of machine learning, deep learning, and applied AI Experience across the full ML lifecycle: data curation, training, deployment, monitoring A track record