Research & Science

Datadog AI Research — Scholars Program with Carnegie Mellon University Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR. You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions. The Lab And The Research: DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact. Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure. What You'll Do: Own a research project end to end: framing the question, running the experiments, writing it up Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab Publish, and use the work toward your dissertation See research reach production, when it works Who You Are: Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field Depth in at least one area relevant to the research above Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers Evidence you can do research: conference or workshop papers, preprin

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree in mathematical sciences ( Operations Research, Computer Science, Statistic

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Data and analytics are at the heart of Lyft’s products and decision-making. As a member of the Lyft Urban Solutions team, you will play a key role in shaping the future of bikeshare by leveraging data to operational needs and city partners. In this role, you will collaborate closely with Operations, Science, and Finance teams to drive data-driven strategies that align our operations with city transportation goals. You will work in a fast-paced environment where analytical insights directly impact fleet management, operator performance, pricing, rider accessibility, and long-term investments in bikesharing infrastructure. We’re looking for a passionate and driven Data Analyst Intern to tackle some of the most complex and impactful challenges in bikesharing. If you’re excited about shaping the future of urban mobility through data, we’d love to hear from you. Responsibilities: Partner with Operations, Engineering, Data Science & Analytics, Product, Finance and other cross-functional stakeholders to manage and update key business logic Develop frameworks and scalable processes to streamline reporting, drive decision-making and prioritization Define the metrics used to measure the success of strategic initiatives and system health; build dashboards to monitor metrics performance over time Work closely with cross-functional partners to deliver data quickly, reliably and accurately to our city partners Monitor and diagnose KPI performance and present findings to senior leadership Experience: Currently pursuing a Bachelors or Masters degree in mathematical sciences (Operations Research, Computer Science, Statistics, Applied Mathematics, Theoretical Physics, Behavioral Science, Electrical Engineering, etc.), Economics (Microeconomics Theory, Econometrics etc.), Data Engineering; or a related field;

Scale AI builds the data infrastructure that powers the world’s most advanced AI. We are the trusted data partner behind frontier model makers and enterprise AI teams — providing the high-quality training data, evaluation frameworks, and human-feedback systems that make models smarter, safer, and more capable. Scale operates as a two-sided marketplace. On the demand side, our customers — leading AI labs and enterprises — need precisely labeled, expert-curated data to train and evaluate their models. On the supply side, we work with a global network of 500,000+ skilled contributors across 100+ countries who perform the complex annotation, evaluation, and data-generation tasks that fuel AI progress. Product Managers at Scale sit at the intersection of these two sides, shaping the systems, tooling, and experiences that make this marketplace work at unprecedented quality and scale. We are hiring Product Managers across multiple teams within our GenAI organization. These roles span both demand-side products (the tools and platforms our customers interact with) and supply-side products (the systems that power our contributor ecosystem). Each role offers the opportunity to work on high-impact, technically complex problems at the frontier of AI — with dedicated engineering, design, and data science teams. About the Role Scale’s GenAI platform is how the world’s leading AI companies — from frontier model labs to Fortune 500 enterprises — create the training data that makes their models best-in-class. As a PM here, you are building the systems that directly shape AI model quality. These roles are deeply cross-functional. You will work with dedicated engineering, design, ML research and data science teams, as well as operations, finance, growth, and customer-facing stakeholders. The problems are technically complex, the pace is fast, and the impact is measurable. Whether you are on the demand side (shaping the products customers use to create and evaluate training data) or the

Welcome systems thinkers. System builds software to help the world see and solve anything as a system, starting in healthcare. We are a Public Benefit Corporation driven by purpose and shaped by values. We hire systems thinkers who are motivated by our purpose, share our values, and have the skills to advance our mission. At System, this means designing the pipelines, platforms, and model-serving systems that power our healthcare data products — reliably, responsibly, and at production grade. As a Data & AI/ML Engineer at System, you will: Design and maintain scalable data pipelines and ETL/ELT workflows Build and operate infrastructure for training, deploying, and serving ML models in production Develop feature stores, vector databases, and other AI-enabling data infrastructure Ensure reliability, low latency, and high availability of data systems Partner closely with Research and Data Science to move findings into production Implement monitoring and observability for data and model health Contribute to infrastructure as code practices and documentation on cloud platforms The ideal candidate has: Bachelor's or Master's degree in Computer Science, Engineering, or a related field 4+ years of experience in data engineering, ML engineering, or a related discipline Strong proficiency in Python and SQL; experience with Spark, dbt, or Airflow a plus Experience building and maintaining cloud data infrastructure (AWS, GCP, or Azure) Understanding of ML lifecycle management, model versioning, and deployment patterns Comfort with systems design principles applied to data-intensive architectures Bonus if you have: Experience with containerization and orchestration (Docker, Kubernetes) Familiarity with knowledge graphs, graph databases, or semantic data models Experience with data migrations while maintaining service availability Background working with clinical or healthcare data Exposure to LLMOps or AI governance frameworks You might be a fit if you: Think i

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments. Preferred Qualifications: Currently pursuing a PhD in computer science, machine learning, or a related field. A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas. Experience developing and evaluating large-scale models or machine learning systems. Familiarity with distributed training, lar