Hong Kong

Flow Traders is looking for a Senior Research Engineer to join our Hong Kong office. This is a unique opportunity to join a leading proprietary trading firm with an entrepreneurial and innovative culture at the heart of its business. We value quick-witted, creative minds and challenge them to make full use of their capacities.

As a Senior Research Engineer, you will be responsible for helping to lead the development of our trading model research framework and using it to conduct research to develop models for trading in production. You'll expand the framework to become global standard way of training, consuming, combining, and transforming any data source in a data-driven systematic way. You will then partner with Quantitative Researchers to build the trading models themselves.

What You Will Do

• Help to lead the development and global rollout of our research framework for defining and training models through various optimization procedures (supervised learning, backtesting etc.), as well as its integration with our platform for deploying and running those models in production
• Partner with Quantitative Researchers to conduct research: test hypotheses and tune/develop data-driven systematic trading strategies and alpha signals

What You Need to Succeed

• Advanced degree (Master's or PhD) in Machine Learning, Statistics, Physics, Computer Science or similar
• 8+ years of hands-on experience MLOps, Research Engineering, or ML Research
• A strong background in mathematics and statistics
• Strong proficiency in programming languages such as Python, with experience in libraries like numpy, pytorch, polars, pandas, and ray.
• Demonstrated experience in designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring
• Understanding of and experience with modern software development practices and tools (e.g. Agile, version control, automated testing, CI/CD, observability)
• Understanding of cloud platforms (e. g., AWS, Azure, GCP) and containerization technologies (e. g., Docker, Kubernetes)

Locations: New York, Chicago, London, Amsterdam, Hong Kong, Sydney

IMC is looking for experienced quant researchers to develop high to mid frequency trading strategies and predictive models for the global markets. If you’re excited about helping to push the boundaries of what we can do with Machine Learning in trading, unlocking the significant edges we have in execution, and collaborating to become the best trading firm worldwide, there's a role for you at IMC.

Research Fellow

Job Reference: 521360
Employment Type: Full Time (Fixed Term, 2 Years)
Location: Perth, Western Australia

Remuneration

Base salary: Level B, $118,150–$139,812 p.a. (pro-rata) plus 17% superannuation

The Research Centre

The Planning and Transport Research Centre (PATREC) at UWA conducts research with direct application to transport planning and road safety. RoadSense Analytics (RSA) is a video analytics platform for traffic analysis, developed through seven years of sustained R&D. The platform translates Australian research into a market-ready product for transport planning applications.

The Role

You will lead research and development of advanced computer vision models, multi-object tracking, and post-processing methods to improve traffic video analytics in complex environments. You will drive benchmarking, evaluation, and deployment optimisation of AI models, ensuring scalability and real-world performance. You will publish research, mentor junior staff, and collaborate with engineers and partners to translate innovations into production-ready solutions.

Selection Criteria

Essential:

• Tertiary degree in Computer Science, Applied Mathematics/Statistics, Robotics, Physics, or related discipline, with excellent academic record
• Demonstrated expertise in computer vision and machine learning, including object detection, segmentation, and multi-object tracking in challenging conditions such as occlusions, crowded scenes, and object re-identification
• Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and Python ML libraries (e.g., NumPy, OpenCV, scikit-learn)
• Experience implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based approaches
• Proven ability to design and run rigorous experimental frameworks, including benchmarking, ablation studies, and field validation

Further Information

Position Description: PD [Research Fellow] [521360].pdf

Contact: Associate Professor Chao Sun
Email: chao.sun@uwa.edu.au

About AIMATX

AIMATX is a Berkeley-based startup revolutionizing materials science by creating next-generation materials and molecules that power the future economy. Our AI-driven platform explores vast chemical spaces, predicts new materials and their properties, and accelerates discovery through intelligent, targeted experimentation. By reducing years of R&D to weeks, we are shaping the future of materials innovation; come join us!

AIMATX is built and guided by a world-class team at the intersection of science, AI and engineering. Our leadership includes Omar Yaghi (2025 Nobel Prize), Fernando Perez, inventor of Jupyter/IPython, alongside former CEOs of public companies and leading researchers in generative AI and autonomous experimentation. This ecosystem brings unmatched scientific depth, computational expertise, and entrepreneurial excellence to accelerate the future of discovery.

Role Overview

We are seeking a Machine Learning Engineer with deep expertise in large-scale generative models (e.g., LLMs, diffusion models) to join our innovative team. You will design, build, and scale the core AI systems that power our materials discovery engine, enabling rapid experimentation, robust deployment, and continuous improvement. As part of our technical team, you will:

• Design and implement training pipelines for LLMs, diffusion models and related architectures for molecular, materials and experimental design.
• Build robust data pipelines and preprocessing workflows for multimodal scientific data.
• Optimize model training and inference at scale, including distributed training and mixed-precision acceleration.
• Develop evaluation, benchmarking and monitoring frameworks to assess reliability, calibration and performance of generative models.
• Collaborate with scientists and engineers to integrate models into self-driving lab workflows and closed-loop experimentation.
• Work closely with MLOps and platform teams to ensure reproducibility, experiment tracking and scalable deployment.
• Stay current with advances in LLMs, diffusion models, reinforcement learning and agentic AI, and translate promising ideas into production systems.
• Maintain high engineering standards, including testing, documentation and code review.

Required Qualifications

• Degree in Computer Science, Machine Learning, Applied Mathematics, Engineering or a related technical field (or equivalent practical experience).
• Strong software engineering experience building and maintaining ML systems in production.
• Expertise with deep learning frameworks such as PyTorch or JAX.
• Proficiency with Python and experience working in collaborative, large-scale codebases.
• Demonstrated track record of owning and delivering end-to-end ML projects from prototype to production.

Preferred Qualifications

• Experience working with generative models in chemistry or materials science.
• Background or strong interest in scientific domains (chemistry, materials science, physics, biology) or scientific ML.
• Contributions to open-source ML or infrastructure projects, or publications in ML/AI conferences or journals.
• Expertise in training large-scale generative models (e.g., LLMs, diffusion models).

Soft Skills & Cultural Fit

• Excellent written and verbal communication skills.
• Collaborative mindset and ability to work effectively in a multidisciplinary team.
• Proactive and self-motivated, with the ability to take initiative.
• Commitment to scientific rigor, innovation and continuous learning.

Benefits & Perks

We offer a competitive salary with bonus potential and meaningful early equity. Compensation reflects experience, expertise and expected impact.

Additional benefits may include: - Flexible work arrangements and remote options. - Medical, dental, and vision coverage. - 401(k) with company matching. - Generous PTO and parental leave.

How to Apply

Send your CV to theo.jaf@aimatx.ai

Introduction of School (located in Shenzhen, China)

The School of Artificial Intelligence (SAI) of The Chinese University of Hong Kong, Shenzhen was founded on February 13, 2025. (https://sai.cuhk.edu.cn/)

The School is committed to addressing the critical national strategic needs and advancing the international frontiers in artificial intelligence. Rooted in the strategic development plan of the Guangdong-Hong Kong-Macao Greater Bay Area and leveraging the industrial strengths of the vibrant Shenzhen Special Economic Zone, the School builds on the solid academic foundation of The Chinese University of Hong Kong, Shenzhen to advance AI foundations and technologies. With a strong emphasis on internationalization, the School aims to create an interdisciplinary and innovative learning environment that equips students with a solid foundation in computer science, mathematics, statistics, and cognitive science, enabling them to fully grasp the principles and applications of AI.

The School strives to elevate China’s teaching and research capabilities in AI through a series of systematic, original breakthroughs with significant international impact. It aims to nurture top-tier professionals and interdisciplinary talents for the nation, the Greater Bay Area, and Shenzhen, providing a continuous supply of talent and research support for the sustainable development of the AI industry.

Post Specification (Application link: https://jobs.cuhk.edu.cn/jobDetails?id=285, contact us: talent4sai@cuhk.edu.cn)

Newly established, The School of Artificial Intelligence (SAI) at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) is pleased to invite applications for full-time faculty positions at all academic ranks with multiple positions. We welcome candidates with expertise in, but not limited to, the following areas:

l Natural Language Processing (NLP): Large language models (LLMs), sentiment analysis, multilingual AI, and generative AI.

l Machine Learning and AI Foundations: Reinforcement learning, transfer learning, representation learning, trustworthy/explainable AI, and computational cognitive science.

l Embodied AI: Human-robot interaction, multi-modal perception, and AI for autonomous decision-making systems.

l Large-scale AI and Distributed Machine Learning: Scalable AI systems, distributed computing architectures, and multi-agent learning frameworks.

l Robotics and Autonomous Systems: Motion planning, intelligent navigation, embodied intelligence, robot design, nano-robots, swarm robotics, medical robotics, and AI for dynamic environments.

l AI Ethics and Society: Fairness, accountability, transparency, and the social implications of AI technologies.

l Emerging AI Paradigms: Neuro-symbolic AI, lifelong learning, quantum AI, and cognitive-inspired computing models.

l AI and Applications: AI for Science, AI for Business and Economics, AI for Medicine, AI for Society, etc.

l Signal Processing: AI-driven statistical signal processing, graph signal processing, and data-driven signal analysis for complex systems.

l Mathematical Sciences: Statistics, advanced computational mathematics, numerical optimization techniques, and mathematical foundations for AI and machine learning.

Qualified candidates have a doctoral degree in Computer Science, Electrical Engineering, Mathematical Science, and other related fields, complemented by postdoctoral training and relevant work experience in related research areas. Applicants for professorships at the rank of Professor or Associate Professor are expected to have an established research program with a funding history, independent research and teaching experiences, and an excellent publication record. We also welcome candidates with teaching experience in related fields for Lecturer and Assistant Professor (Teaching) positions.

New York

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 Machine Learning (ML) 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 MLOps Engineers with strong expertise and passion for building and maintaining AI systems to join our team.

As a Senior MLOps Engineer you will design and build tools to improve the efficiency of our Model Development Life Cycle (MDLC), automate ML processes, enhance the performance of our systems and more.

Join the AI Group as a Senior MLOps Engineer and you will have the opportunity to: -Architect, build, and diagnose production AI applications and systems -Collaborate with colleagues on production systems and write, test, and maintain production quality code -Define and provide strong SLAs around latency, throughput and resource (memory / disk / network / CPU / GPU) usage -Work closely with AI Platform teams to operationalize continuous model training, inference, and monitoring workflows

We are looking for a Senior MLOps engineer with:

-4+ years of experience working with an object-oriented programming language (Python, Go, etc.) -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 -Industry experience with machine learning teams -Working knowledge of common ML frameworks such as PyTorch, ONNX, DeepSpeed etc. -Prior experience with cloud-native technologies like Kubernetes, Argo Workflows, Buildpacks, etc. -Experience with cloud providers such as AWS, GCP or Azure -A track record of collaboration with colleagues to achieve repeatable high quality outcomes

Successful hires will support our machine learning team by processing and organizing scientific datasets relevant to drug discovery and development. Candidates should have an undergraduate degree in a STEM field, hands-on experience in a Linux environment, and familiarity with Python and SQL. Experience handling large-scale datasets, structuring data from diverse sources, and cataloging metadata to facilitate data discovery and maintain accurate data provenance is highly desirable. This is a two-year position with full benefits. For more information, visit www.DEShawResearch.com.

Please apply using the link below:

https://apply.deshawresearch.com/careers/Register?pipelineId=909&source=NeurIPS_1

The expected annual base salary for this role is USD 160,000 - USD 200,000. Our compensation package includes variable compensation in the form of sign-on and year-end bonuses, as well as generous benefits, including relocation and immigration assistance. The final salary will be determined based on multiple factors, including prior experience and educational background. We follow a hybrid work schedule, with employees working in-office from Tuesday through Thursday and having the option to work remotely on Monday and Friday.

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

Various locations available

Adobe is looking for a Machine Learning intern who will apply AI and machine learning techniques to big-data problems to help Adobe better understand, lead and optimize the experience of its customers.

By using predictive models, experimental design methods, and optimization techniques, you will be working on the research and development of exciting projects like real-time online media optimization, sales operation analytics, customer churn scoring and management, customer understanding, product recommendation and customer lifetime value prediction.

All 2026 Adobe interns will be co-located hybrid. This means that interns will work between their assigned office and home. Interns will be based in the office where their manager and/or team are located, where they will get the most support to ensure collaboration and the best employee experience. Managers and their organization will determine the frequency they need to go into the office to meet priorities.  

What You’ll Do
- Develop predictive models on large-scale datasets to address various business problems with statistical modeling, machine learning, and analytics techniques. - Develop and implement scalable, efficient, and interpretable modeling algorithms that can work with large-scale data in production systems - Collaborate with product management and engineering groups to develop new products and features.

What You Need to Succeed
- Currently enrolled full time and pursuing a Master’s or PhD degree in Computer Science, Computer Engineering; or equivalent experience required with an expected graduation date of December 2026 – June 2027 - Good understanding of statistical modeling, machine learning, deep learning, or data analytics concepts. - Proficient in one or more programming languages such as Python, Java and C - Familiar with one or more machine learning or statistical modeling tools such as R, Matlab and scikit learn - Strong analytical and quantitative problem-solving ability. - Excellent communication, relationship skills and a team player - Ability to participate in a full-time internship between May-September

San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

Within the Ads Delivery team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners. We are looking for a Machine Learning Engineer/Economist with a strong theoretical and data analysis background that understands market design concepts and has the engineering skills to bring them to market. We are looking for an economist who can get their hands dirty and work side by side with other engineers, to advance the efficiency of the Pinterest Marketplace. The nature of projects within this team require a deep understanding of trade-offs, founded on both economic theory and data analysis, from the ideation phase all the way to launch review.

What you’ll do:

• Build statistical models and production systems to improve marketplace design and operations for Pinners, Partners, and Pinterest.
• Tune marketplace parameters (e.g., utility function), optimize ad diversity and load, implement auctions, and model long‑term effects to reduce ad fatigue and improve advertiser outcomes.
• Define and implement experiments to understand long term Marketplace effects.
• Develop strategies to balance long and short term business objectives.
• Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations.
• Work across application areas such as marketplace performance analysis, advertiser churn/retention modeling, promotional bandwidth allocation, ranking/pricing/mechanism design, bidding/budgeting innovation, and anticipating second‑order effects for new ad offerings.

What we’re looking for:

• Degree in Computer Science, Machine Learning, Economics, Operations Research, Statistics or a related field.
• Industry experience in applying economics or machine learning to real products (e.g., ads auctions, pricing, marketplaces, or large‑scale recommendation/search systems).
• Knowledge in auction theory, market design, and econometrics with excellent data analysis skills.
• Strong software engineering and mathematical skills and proficiency with statistical methods.
• Experience with online experimentation and causal inference (A/B testing, long‑running experiments, or similar) in large‑scale systems.
• Practical understanding of machine learning algorithms and techniques.
• Impact‑driven, highly collaborative, and an effective communicator; prior ads or two‑sided marketplace experience strongly preferred.

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