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.

Location Bay area or remote

Description

Goaly AI is hiring multiple AI research & AI infra positions, full-time and intern!

=== About Goaly === We are an early-stage stealth mode AI startup located in Silicon Valley, founded by ex-FAANG AI engineers & researchers who have led multiple GenAI products from research to production, powering billion-user products. We are backed by accredited investors and AI leadership from top tech firms, primarily serving rising AI labs and enterprise clients. We are on a mission to make frontier AI accessible and affordable to every business.

=== Why This Matters for Your Research === You will own end-to-end LLM/SLM development, working on cutting-edge model architectures and novel optimization techniques. Your research is backed by abundant GPU clusters, and we offer full support to publish breakthrough results at top conferences, including NeurIPS, ICML, and CVPR.

We are a super fun team that work and play hard together. We are actively hiring AI researchers and AI infra engineers (intern and full-time). Our job postings: https://goaly.ai/jobs?job_function=research
Send your cool projects / resume to recruiting@goaly.ai and lets YOLO together!

Amsterdam

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.

Bala Cynwyd (Philadelphia Area), Pennsylvania United States & New York, New York United States

Overview

Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and markets.

As a Machine Learning Intern at Susquehanna, you’ll work on high-impact projects that closely reflect the challenges and workflows of our full-time research team. You’ll apply your technical expertise in machine learning and data science to real-world financial problems, while developing a deep understanding of how machine learning integrates into Susquehanna’s research and trading systems. You will leverage vast and diverse datasets and apply cutting-edge machine learning at scale to drive data-informed decisions in predictive modeling to strategic execution.

What You Can Expect

 • Conduct research and develop ML models to identify patterns in noisy, non-stationary data

 • Work side-by-side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting-edge ML research and practical implementation

 • Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches

 • Design and run experiments using the latest ML tools and frameworks

 • One-on-one mentorship from experienced researchers and technologists

 • Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices

 • Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior

 • Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk-aware decision making

What we're looking for

 • Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field

 • Proven experience applying machine learning techniques in a professional or academic setting

 • Strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR

 • Hands-on experience with machine learning frameworks, including PyTorch and TensorFlow

 • Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment

Why Join Us?

 • Work with a world-class team of researchers and technologists

 • Access to unparalleled financial data and computing resources

 • Opportunity to make a direct impact on trading performance

 • Collaborative, intellectually stimulating environment with global reach

Redwood City, CA

Biohub is leading the new era of AI-powered biology to cure or prevent disease through its 501c3 medical research organization, with the support of the Chan Zuckerberg Initiative.

Biohub supports the science and technology that will make it possible to help scientists cure, prevent, or manage all diseases by the end of this century. While this may seem like an audacious goal, in the last 100 years, biomedical science has made tremendous strides in understanding biological systems, advancing human health, and treating diseour organization and research partners all for the purpose of contributing to greater understanding of human cell function.

You will have the opportunity to work closely with teams of scientists, computational biologists, engineers and to collaborate with our grantees, with our institutes, and other external labs and organizations. Your work will inspire and enhance the production and analysis of datasets by teams and collaborators. Scientific focus areas could include single cell biology, imaging, genomics, and proteomics.

What You'll Do Working with the AI Research Scientists, iterate on, optimize, deploy, and maintain innovative machine learning models, systems, and software tools that enable the analysis and interpretation of AI models for Biology Work with cross-functional team members to quickly iterate on system performance to meet/stay ahead of users’ needs - e.g. we get feedback that the model doesn't scale to X million so working with our user researcher/scientist/product team to iterate on the solution. Partner with research scientists to build robust data loader pipelines for scalable distributed training and evaluation. Serve as an interface to product and engineering teams to understand how models may need to evolve to support multiple use cases. Develop model evaluation and interpretability frameworks that help biologists understand which data features drive model predictions Build reusable engineering utilities that can unlock experimentation velocity across research initiatives in the organization Optimize model architectures to enhance performance, fine-tune accuracy, and efficiently manage infrastructure resources

What You'll Bring Experience in working with a highly interactive and cross-functional collaborative environment with a diverse team of colleagues and partners solving complex problems through applied deep learning. A track record and expertise in developing deep learning models on large-scale GPU clusters, using techniques of distributing training such as DDP, FSDP, Model parallelism, low-precision training, profiling and optimizing AI/ML code, fine tuning models. Expertise in leading end-to-end experimentation pipelines for training and evaluating deep learning models, with particular focus on experiment tracking and reproducibility. A good working knowledge of Python-based ML libraries and frameworks such as PyTorch, JAX, TensorFlow, NumPy, Pandas, and Scikit-learn. Experience in using modern frameworks for distributed computing and infrastructure management, particularly as related to ML models such as PyTorch Lightning, Deepspeed, TransformerEngine, RayScale etc. Ability to effectively balance exploratory research with robust engineering practices. A good working knowledge of general software engineering practices in a production environment. The ability to work independently and as part of a team, and have excellent communication and interpersonal skills. Have a Masters in computer science with a focus on machine learning & data analytics, or equivalent industry experience and at least 6-8 years of experience developing and applying machine learning methods.

Work Location:

Toronto, Ontario, Canada

Description

We are currently seeking talented individuals for a variety of positions, ranging from junior to senior levels, and will evaluate your application in its entirety.

Layer 6 is the AI center of excellence for TD Bank Group. We develop and deploy industry-leading machine learning systems that impact the lives of over 27 million customers, helping more people achieve their financial goals and needs. Our work spans the field of machine learning with areas such as deep learning and generative AI, time series forecasting and responsible use of AI. We have access to massive financial datasets and actively collaborate with world renowned academic faculty. We are driven to be at the cutting edge of machine learning in research, engineering, and impactful applications.

We are looking for world-class engineers to tackle cutting-edge problems in Machine Learning applied to the real world. Work with large-scale, real-world datasets spanning multiple modalities, ranging from banking transactions, conversation transcripts to large document collections.

As a Machine Learning Engineer, you will:

• Join a world-class team of AI developers with an extensive track record of shipping solutions at the cutting-edge

• Architect scalable machine learning and Gen AI systems that integrate with existing data platform and infrastructure, focusing on automation, operation efficiency, and reliability

• Write clean, efficient, and maintainable code for ML models to ensure efficient deployment of scalable AI application

• Work with large-scale, real-world datasets that range from banking transactions, conversation histories, to large document collections

• Grow by continuously learning new skills and exploring advanced topics in AI with a team that thrives on knowledge-sharing

Required Qualifications:

• Master or bachelor's degree in computer science, Statistics, Mathematics, Engineering or a related field

• 3+ years of developer experience shipping code in production settings

• Strong background in machine learning and deep learning

• Strong coding proficiency in Python, Java, C, or C++ You value good software design and sweat over details in code and API design

• You take great personal pride in building robust and scalable software

• You are highly accountable and have a strong sense of ownership

• You strive to communicate clearly and with empathy

Preferred Qualifications:

• Research experience with publication record

• Experience with LangGraph, Pytorch, Tensorflow, Jax, or comparable library

• Experience with building and scaling data-intensive software

• Experience using GPUs for accelerated deep learning training

We are Bagel Labs - a distributed machine learning research lab working toward open-source superintelligence.

Role Overview

You will design and optimize distributed diffusion model training and serving systems.
Your mission is to build scalable, fault-tolerant infrastructure that serves open-source diffusion models across multiple nodes and regions with efficient adaptation support.

Key Responsibilities

• Design and implement distributed diffusion inference systems for image, video, and multimodal generation.
• Architect high-availability clusters with failover, load balancing, and dynamic batching for variable resolutions.
• Build monitoring and observability systems for denoising steps, memory usage, generation latency, and CLIP score tracking.
• Integrate with open-source frameworks such as Diffusers, ComfyUI, and Invoke AI.
• Implement and optimize rectified flow, consistency distillation, and progressive distillation.
• Design distributed systems for ControlNet, IP-Adapter, and multimodal conditioning at scale.
• Build infrastructure for LoRA/LyCORIS adaptation with hot-swapping and memory-efficient merging.
• Optimize VAE decoding pipelines and implement tiled/windowed generation for ultra-high-resolution outputs.
• Document architectural decisions, review code, and publish technical deep-dives on blog.bagel.com.

Who You Might Be

You understand distributed systems and diffusion architectures deeply.
You’re excited about the evolution from DDPM to flow matching to consistency models, and you enjoy building infrastructure that handles complex, variable compute workloads.

Required Skills

1. 5+ years in distributed systems or production ML serving.
2. Hands-on experience with Diffusers, ComfyUI, or similar frameworks in production.
3. Deep understanding of diffusion architectures (U-Net, DiT, rectified flows, consistency models).
4. Experience with distributed GPU orchestration for high-memory workloads.
5. Proven record of optimizing generation latency (CFG, DDIM/DPM solvers, distillation).
6. Familiarity with attention optimization (Flash Attention, xFormers, memory-efficient attention).
7. Strong grasp of adaptation techniques (LoRA, LyCORIS, textual inversion, DreamBooth).
8. Skilled in variable-resolution generation and dynamic batching strategies.

Bonus Skills

• Contributions to open-source diffusion frameworks or research.
• Experience with video diffusion models and temporal consistency optimization.
• Knowledge of quantization techniques (INT8, mixed precision) for diffusion models.
• Experience with SDXL, Stable Cascade, Würstchen, or latent consistency models.
• Distributed training using EDM, v-prediction, or zero-terminal SNR.
• Familiarity with CLIP guidance, perceptual loss, and aesthetic scoring.
• Experience with real-time diffusion inference (consistency or adversarial distillation).
• Published work or talks on diffusion inference optimization.

What We Offer

• Top-of-market compensation
• A deeply technical culture where bold ideas are built, not just discussed
• Remote flexibility within North American time zones
• Ownership of work shaping decentralized AI
• Paid travel to leading ML conferences worldwide

Apply now - help us build the infrastructure for open-source superintelligence.

The role We are seeking a highly skilled and customer-focused professional to join our team as a Cloud Solutions Architect specializing in Cloud infrastructure and MLOps. As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines.

You’re welcome to work remotely from the US or Canada.

Your responsibilities will include: - Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices. - Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers. - Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI. - Act as a single point of expertise of customer scenarios for product, technical support, marketing teams. - Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.)

We expect you to have: - 5 - 10 + years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing - Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python - Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL) - Excellent communication skills - Customer-centric mindset

It will be an added bonus if you have: - Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow) - Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch) - Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google)

San Jose, CA, USA

We are seeking a creative and technically skilled Prompt Engineer to enhance large language model (LLM) performance across business-critical workflows. This position centers on designing, testing, and integrating strategies that drive intelligent agents and enterprise use cases. You will work closely with AI engineers, product teams, and domain experts to guarantee scalable, safe, and high-accuracy AI applications.

What you'll Do - Prompt Strategy & Design: Develop templates and multi-step chains tailored to specific business functions (e.g., sales enablement, support, knowledge management). Develop few-shot, zero-shot, and hybrid patterns for enhanced reasoning and context retention. Maintain libraries for reuse and version control.

• Function Calling & Tool Use: Implement LLM function calling to trigger APIs, databases, or internal tools. Build tool-use pipelines within agent workflows for complex task automation.

• Conversation Flow & Persona Design: Define and build agent personas, roles, and behaviors for domain-specific applications. Manage multi-turn conversations, memory handling, and contextual continuity.

• Enterprise-grade Optimization: Tailor prompts for performance in enterprise environments, prioritizing accuracy, privacy, fairness, and compliance. Collaborate with legal and security teams to mitigate hallucination, bias, and misuse risks.

• Testing & Evaluation: Tailor prompts for performance in enterprise environments, prioritizing accuracy, privacy, fairness, and compliance. Collaborate with legal and security teams to mitigate hallucination, bias, and misuse risks.

• Deployment & Integration: Partner with AI Agent Engineers to integrate prompts into agent workflows and orchestration pipelines. Maintain documentation and workflows for deployment in production environments.

What you need to succeed - 3+ years of experience in NLP, AI/ML product development, or application scripting - Strong grasp of LLM capabilities and limitations (e.g., OpenAI, Claude, Mistral, Cohere) - Experience crafting prompts and evaluation methods for enterprise tasks - Familiarity with frameworks like LangChain, Semantic Kernel, or AutoGen - Strong Python and API integration skills - Excellent written communication and structured thinking

Preferred Qualifications - Experience with LLM function calling, custom tool integration, and agent workflows - Background in UX writing, human-computer interaction, or instructional design - Understanding of enterprise compliance (e.g., SOC 2, GDPR) in AI systems - Bachelor's or equivalent experience in Computer Science, Computational Linguistics, Cognitive Science, or a related field

London

Description - Bloomberg’s Engineering AI department has 350+ AI practitioners building highly sought after products and features that often require novel innovations. We are investing in AI to build better search, discovery, and workflow solutions using technologies such as transformers, gradient boosted decision trees, large language models, and dense vector databases. We are expanding our group and seeking highly skilled individuals who will be responsible for contributing to the team (or teams) of Artificial Intelligence (AI) and Software Engineers that are bringing innovative solutions to AI-driven customer-facing products.

At Bloomberg, we believe in fostering a transparent and efficient financial marketplace. Our business is built on technology that makes news, research, financial data, and analytics on over 1 billion proprietary and third-party data points published daily -- across all asset classes -- searchable, discoverable, and actionable.

Bloomberg has been building Artificial Intelligence applications that offer solutions to these problems with high accuracy and low latency since 2009. We build AI systems to help process and organize the ever-increasing volume of structured and unstructured information needed to make informed decisions. Our use of AI uncovers signals, helps us produce analytics about financial instruments in all asset classes, and delivers clarity when our clients need it most.

We are looking for Senior GenAI Platform Engineers with strong expertise and passion for building platforms, especially for GenAI systems.

As a Senior GenAI Platform Engineer, you will have the opportunity to create a more cohesive, integrated, and managed GenAI development life cycle to enable the building and maintenance of our ML systems. Our teams make extensive use of open source technologies such as Kubernetes, KServe, MCP, Envoy AI Gateway, Buildpacks and other cloud-native and GenAI technologies. From technical governance to upstream collaboration, we are committed to enhancing the impact and sustainability of open source.

Join the AI Group as a Senior GenAI Platform Engineer and you will have the opportunity to: -Architect, build, and diagnose multi-tenant GenAI platform systems -Work closely with GenAI application teams to design seamless workflows for continuous model training, inference, and monitoring -Interface with both GenAI experts to understand workflows, pinpoint and resolve inefficiencies, and inform the next set of features for the platforms -Collaborate with open-source communities and GenAI application teams to build a cohesive development experience -Troubleshoot and debug user issues -Provide operational and user-facing documentation

We are looking for a Senior GenAI Platform Engineer with: -Proven years of experience working with an object-oriented programming language (Python, Go, etc.) -Experience with GenAI technologies like MCP, A2A, Langgraph, LlamaIndex, Pydantic AI, OpenAI APIs and SDKs -A Degree in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience -An understanding of Computer Science fundamentals such as data structures and algorithms -An honest approach to problem-solving, and ability to collaborate with peers, stakeholders and management