NeurIPS 2025 Career Opportunities

Global: United States, Europe and Asia

At Citadel, our mission is to be the most successful investment team in the world. Quantitative Researchers play a key role in this mission by developing next-generation models and trading approaches for a range of investment strategies. You’ll get to challenge the impossible in quantitative research by applying sophisticated and complex statistical techniques to financial markets, some of the most complex data sets in the world. As an intern, you’ll get to challenge the impossible in research through an 11 week program that will allow you to collaborate and connect with senior team members. In addition, you’ll get the opportunity to network and socialize with peers throughout the internship. Our signature internship program takes place June through August. Occasionally, we can be flexible to other times of the year. You will be able to indicate your timing preference in the application.

New York, New York

We are looking for a foundational member of the Cloud infrastructure team at WRITER. This role will involve contributing to the development and implementation of our Site reliability engineering (SRE) program. The ideal candidate will ensure the reliability, scalability, performance, and security of WRITER’s critical systems, taking a proactive approach to guarantee that our high-ROI products reach our customers seamlessly.

Your responsibilities:

• Lead the design, implementation, and maintenance of WRITER, Inc.’s cloud infrastructure to ensure high availability and performance
• Design and implement scalable cloud automation to support seamless deployment for our largest enterprise customers
• Automate infrastructure provisioning and management using Terraform & Python
• Collaborate with development teams to optimize cloud resources and enhance system reliability
• Develop and maintain monitoring and alerting systems to proactively identify and resolve issues affecting the reliability of our writing solutions
• Conduct post-mortem analyses of system failures to identify root causes and implement preventive measures
• Optimize and scale our cloud infrastructure to support growing user demand and ensure cost efficiency
• Ensure the security and compliance of our systems, adhering to industry standards and regulations
• Provide mentorship and technical guidance to junior engineers, fostering a culture of reliability and continuous improvement
• Stay current with emerging technologies and industry trends to continuously improve our site reliability practices

Is this you?

• Proven expertise in Site Reliability Engineering with a minimum of 7 years of hands-on experience
• Deep understanding of system architecture and infrastructure design to ensure high availability and performance
• Bachelor’s degree in Computer Science, Engineering, or a related technical field
• Strong proficiency in programming languages such as Python, Java, Go for automation and monitoring
• Experience with cloud platforms like AWS, Azure, or GCP, and their respective services for scalable and resilient systems
• Expertise in containerization technologies (e.g., Docker, Kubernetes) and orchestration tools
• Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to maintain system health and performance
• Ability to lead and mentor junior engineers in best practices for reliability and system optimization
• Excellent communication skills to collaborate effectively with cross-functional teams and stakeholder
• Proactive approach to identifying and mitigating potential system failures and performance bottlenecks

San Jose, CA, USA

Join Adobe as a skilled and proactive Machine Learning Ops Engineer to drive the operational reliability, scalability, and performance of our AI systems! This role is foundational in ensuring our AI systems operate seamlessly across environments while meeting the needs of both developers and end users. You will lead efforts to automate and optimize the full machine learning lifecycle—from data pipelines and model deployment to monitoring, governance, and incident response.

What you'll Do

• Model Lifecycle Management: Manage model versioning, deployment strategies, rollback mechanisms, and A/B testing frameworks for LLM agents and RAG systems. Coordinate model registries, artifacts, and promotion workflows in collaboration with ML Engineers

• Monitoring & Observability: Implement real-time monitoring of model performance (accuracy, latency, drift, degradation). Track conversation quality metrics and user feedback loops for production agents.

• CI/CD for AI: Develop automated pipelines for timely/agent testing, validation, and deployment. Integrate unit/integration tests into model and workflow updates for safe rollouts.

• Infrastructure Automation: Provision and manage scalable infrastructure (Kubernetes, Terraform, serverless stacks). Enable auto-scaling, resource optimization, and load balancing for AI workloads.

• Data Pipeline Management: Craft and maintain data ingestion pipelines for both structured and unstructured sources. Ensure reliable feature extraction, transformation, and data validation workflows.

• Performance Optimization: Monitor and optimize AI stack performance (model latency, API efficiency, GPU/compute utilization). Drive cost-aware engineering across inference, retrieval, and orchestration layers.

• Incident Response & Reliability: Build alerting and triage systems to identify and resolve production issues. Maintain SLAs and develop rollback/recovery strategies for AI services.

• Compliance & Governance: Enforce model governance, audit trails, and explainability standards. Support documentation and regulatory frameworks (e.g., GDPR, SOC 2, internal policy alignment).

What you need to succeed - 3–5+ years in MLOps, DevOps, or ML platform engineering. - Strong experience with cloud infrastructure (AWS/GCP/Azure), container orchestration (Kubernetes), and IaC tools (Terraform, Helm). - Familiarity with ML model serving tools (e.g., MLflow, Seldon, TorchServe, BentoML). - Proficiency in Python and CI/CD automation (e.g., GitHub Actions, Jenkins, Argo Workflows). - Experience with monitoring tools (Prometheus, Grafana, Datadog, ELK, Arize AI, etc.).

Preferred Qualifications - Experience supporting LLM applications, RAG pipelines, or AI agent orchestration. - Understanding of vector databases, embedding workflows, and model retraining triggers. - Exposure to privacy, safety, and responsible AI principles in operational contexts. - Bachelor's or equivalent experience in Computer Science, Engineering, or a related technical field.

Successful hires will work on developing and applying large language models (LLMs) to problems in molecular science and drug discovery. Responsibilities include:

• Scaling and optimizing large model training and inference workflows on cutting-edge DESRES infrastructure
• Pre-training, including designing data pipelines and distributed/parallel training
• Post-training techniques, such as reinforcement learning, contrastive learning, and instruction tuning
• Multimodal learning and integrating non-text modalities (for example, molecular graphs, 3D structures, and time series)

Ideal candidates will have deep expertise in large-scale machine learning systems, LLM architecture and training, and/or multimodal learning, as well as strong Python programming skills. While the application domains include areas such as drug discovery and biomolecular simulation, specific experience in any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of machine learning. For more information, visit www.DEShawResearch.com.

Please apply using the link below:

https://apply.deshawresearch.com/careers/Register?pipelineId=921&source=NeurIPS_1

The expected annual base salary for this position is USD 300,000 - USD 800,000. Our compensation package also includes variable compensation in the form of sign-on and year-end bonuses, and generous benefits, including relocation and immigration assistance. The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including the nature and extent of prior experience and educational background. We follow a hybrid work schedule, in which employees work from the office on Tuesday through Thursday, and have the option of working from home on Monday and Friday.

D. E. Shaw Research, LLC is an equal opportunity employer.

IMC Trading is seeking a Machine Learning Research Lead with proven experience applying state-of-the-art machine learning to solve challenging trading problems. This role will drive the development of a centralized ML environment to be used across all areas of trading at IMC. The ideal candidate will have experience working with other researchers, traders, and engineers to build and continuously improve a research platform to drive innovation via ML. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics and trading intuition as well as a problem-solving mindset and pragmatism. This is an opportunity to dive deep into feature engineering and focus on applying a wide range of ML models as well as to perform research on building custom models.

New York / Chicago

Flow Traders is committed to leveraging the most recent advances in machine learning, computer science, and AI to generate value in the financial markets. We are looking for Quantitative Researchers to join this challenge.

As a Quantitative Researcher at Flow Traders, you are an expert in mathematics and statistics. You are passionate about translating challenging problems into equations and models, and have the ability to optimize them using cutting-edge computational techniques. You collaborate with a global team of researchers and engineers to design, build, and optimize our next generation of models and trading strategies.

Are you at the top of your quantitative, modeling, and coding game, and excited by the prospect of demonstrating these skills in competitive live markets? Then this opportunity is for you.

Location USA, CA, Sunnyvale USA, WA, Seattle

Description Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads.

Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience.

As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people.

We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you!

We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide.

The College of Information Science at the University of Arizona invites applications for multiple tenure track Assistant Professor positions to start in Fall 2026. We are looking for candidates with expertise in artificial intelligence (AI), specifically in AI-driven cybersecurity or cyber operations, or expertise in trustworthiness, explainability, or fairness in AI.

The University of Arizona is a Research 1 institution (very high research spending and doctorate production) and the College of Information Science supports seven undergraduate degrees and five graduate degrees, including the #3 ranked Cybsersecurity MS, the #4 ranked Data Science MS and the #24 ranked Library and Information Science MA.

Qualifications: * Ph.D. in information science, computer science, computer engineering, cybersecurity or a related field, completed before start date * A strong publication record in respected venues in artificial intelligence and a research agenda with promising future research directions * Expertise in AI-driven cybersecurity or cyber operations; OR expertise in trustworthiness, explainability, or fairness in AI

Apply by December 15, 2025

Successful hires will expand the group's efforts applying machine learning to drug discovery, biomolecular simulation, and biophysics. Areas of focus include generative models to help identify novel molecules for drug discovery targets, predict PK and ADME properties of small molecules, develop more accurate approaches for molecular simulations, and understand disease mechanisms. Ideal candidates will have strong Python programming skills. Relevant areas of experience might include deep learning techniques, systems software, high performance computation, numerical algorithms, data science, cheminformatics, medicinal chemistry, structural biology, molecular physics, and/or quantum chemistry, but specific knowledge of any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of machine learning. For more information, visit www.DEShawResearch.com.

Please apply using this link: https://apply.deshawresearch.com/careers/Register?pipelineId=597&source=NeurIPS_1

The expected annual base salary for this position is USD 300,000 - USD 800,000. Our compensation package also includes variable compensation in the form of sign-on and year-end bonuses, and generous benefits, including relocation and immigration assistance. The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including the nature and extent of prior experience and educational background. We follow a hybrid work schedule, in which employees work from the office on Tuesday through Thursday, and have the option of working from home on Monday and Friday.

D. E. Shaw Research, LLC is an equal opportunity employer.

Remote - Americas

Applied Machine Learning Engineer

At Shopify, our Applied Machine Learning Engineers tackle the most challenging technical problems in commerce. By leveraging vast datasets and cutting-edge machine learning technologies, like multimodal LLMs, embeddings, etc., you will develop ways that redefine how merchants connect with buyers. Your work will empower merchants with advanced tools and systems that enhance search, discovery, and agents at a global scale.

As an Applied Machine Learning Engineer, you will work with petabyte-scale data and utilize state-of-the-art ML methods to build and deploy models that serve millions of users. You'll be at the forefront of AI innovation, using technologies including LLM posttraining, reinforcement learning, and model quantization, to redefine what’s possible in e-commerce.

Key Responsibilities:

• Analyze and interpret large-scale datasets to drive model development and optimization
• Collaborate with cross-functional teams to integrate ML solutions into Shopify's core products
• Design, build, and deploy agents and multimodal LLMs that improve merchant and buyer interactions
• Stay current with the latest advancements in machine learning technologies and frameworks
• Document and share technical insights and best practices across teams

Qualifications:

• Extensive experience in building and deploying machine learning models at scale
• Proficiency in using ML frameworks (e.g., TensorFlow, PyTorch) and programming languages like Python
• Strong analytical and problem-solving skills in solving real-world product problems
• Excellent communication skills and the ability to work in a fast-paced, collaborative environment

This role may require on-call work.

Ready to deploy your next breakthrough model? Join the team that’s making commerce better for everyone.

At Shopify, we pride ourselves on moving quickly—not just in shipping, but in our hiring process as well. If you're ready to apply, please be prepared to interview with us within the week. Our goal is to complete the entire interview loop within 30 days. You will be expected to complete a live pair programming session, come prepared with your own IDE.