Machine Learning Research Intern (Summer 2027)

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

A Career with Point72’s Technology Team As Point72 reimagines the future of investing, our Technology team is constantly improving our company’s IT infrastructure, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts experimenting, discovering new ways to harness the power of open-source solutions, and embracing enterprise agile methodology. We encourage professional development to ensure you bring innovative ideas to our products while satisfying your own intellectual curiosity. About the Software Engineer Internship The Software Engineer Internship at Point72 provides individuals interested in pursuing a career in technology at an investment firm with a valuable, hands-on experience. During the program, you will gain exposure to: Developing and enhancing software applications that support asset management functions, including trading systems, research platforms, and data analysis tools Code reviews, maintaining existing codebases, and ensuring software scalability and integrity Technologies and methodologies like AI, machine learning, big data, and cloud computing to solve complex problems Collaborating with cross-functional teams to ensure technology solutions align with business goals and processes Documenting and presenting design decisions, code, and functionalities to team members and stakeholders Interacting with senior-level professionals The fast-paced, competitive hedge fund world and industry best practices Summer Internship Program at Point72 In addition to the learning and development you’ll receive in your day-to-day role with your team, as a part of our summer internship program you will: Work alongside your fellow interns and be mentored by experienced professionals Meet your peers through designated intern programming, volunteer opportunities, and social events Attend sessions with senior leaders discussing their

A Career with Point72’s Market Intelligence Team On our proprietary research team—Market Intelligence—you’ll support Analytics team members as they work with our investment professionals and Compliance team to uncover insights about companies, industries, and the broader economy through deep fundamental research and applying data science and engineering techniques to alternative data sets. You’ll work alongside a talented team with diverse skills, backgrounds, and perspectives. Our industry researchers, product managers, and data scientists and engineers work together to build compliant research products that answer the questions our investment professionals care about most. We look for other bright, motivated, and collaborative people to join our team and grow with us. About the MS/PhD Data Scientist Internship Our data scientists conduct research through data mining and statistical modeling to discover insights from large, complex data sets that are used by our investment professionals to make investment decisions. During the 10-week program based in our New York office, you will gain exposure to: A highly collegial environment that emphasizes teaching and learning as a team Tackling challenges of featuring and modeling large and unstructured data using machine learning and statistical techniques Managing all aspects of the research and execution process including methodology selection, data collection and quality, modeling and analysis, and performance monitoring Working alongside Analytics team members who deliver research findings to investment teams, portfolio managers, and other internal clients Driving technical innovation through a collaborative R&D process Summer Internship Program at Point72 In addition to the learning and development you’ll receive in your day-to-day role with your team, as a part of our summer internship program you will: Work alongside your fellow interns and be mentored

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

Job Requisition ID # 26WD101374 Position Overview As a Research intern at Autodesk Research, you will explore and apply novel technologies and techniques to empower our customers in designing and building a better, safer, and more sustainable world. We are a team of researchers, engineers, and industry domain experts working on projects that range from learning-based design systems, computer vision, graphics, robotics, human-computer interaction, sustainability, simulation, manufacturing, architectural design and construction. Working with a multidisciplinary research team across AI, spatial computing, AEC, and human perception, you’ll develop and evaluate methods that interconnect different representations of the built environment within immersive, editable world models. Possible research directions may include exploring editing non-traditional 3d geometries such as gaussian splats, building multi-layered spatial representations of architectural spaces (simulations, physics, rendering, reality capture), and controlling 3d modeling and design environments and processes with agentic workflows. You’ll build research prototypes and explore how this work could inform future Autodesk design workflows. Responsibilities Research multimodal AI approaches for the generation and editing of novel 3d representations

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