Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

We are seeking a Software Engineer to join our HPC Research Infrastructure team, responsible for building and evolving an in-house research platform that underpins quantitative research across the firm. The platform provides scalable, secure, and efficient access to high-performance compute and data, enabling researchers to run large-scale, compute-intensive pipelines with minimal friction. 

In this role, you will design and implement core platform components including APIs, backend services, and tooling to support job submission, scheduling, resource management, security, and observability across heterogeneous compute environments (on-prem and/or cloud). You will operate at the intersection of software engineering, distributed systems, and infrastructure, helping to deliver a robust, developer-friendly platform that enables research at scale and has company-wide impact. 

Responsibilities

• Collaborate closely with quantitative researchers, platform engineers, and other cross-functional teams to understand research workflows and translate them into scalable, reliable platform capabilities 
• Design, build, and maintain distributed services and APIs that manage compute workloads, data access, and execution lifecycles across HPC environments 
• Improve the usability, reliability, and performance of large-scale quantitative research workflows, with a strong focus on developer experience and operational excellence 
• Contribute to the design and implementation of scheduling, security, and observability solutions suitable for highly parallel, compute-heavy systems 
• Work with state-of-the-art infrastructure and modern software engineering practices to support massive compute pipelines and rapidly evolving research demands 
• Take ownership of complex technical problems, operating with a high degree of autonomy while making pragmatic trade-offs to deliver impact quickly and safely 

Your Present Skillset 

• BSc / MEng / PhD in Computer Science or a closely related field, or equivalent practical experience, with 5+ years of professional software development 
• Strong proficiency in at least one statically-typed programming language (e.g. C, C++, Java, Rust), with an interest in Go (prior experience welcome but not required). Experience with Python is a plus 
• Required: 5+ years of experience designing, implementing, and operating large-scale distributed systems in production environments 
• Preferred: Prior experience in quantitative finance or working closely with quantitative researchers and data-intensive workloads 
• Preferred Understanding of GPU programming models and performance considerations (e.g. CUDA, memory hierarchies, parallel execution), with exposure to ML frameworks such as PyTorch or TensorFlow 
• Solid understanding of systems concepts such as concurrency, performance, fault tolerance, and reliability 
• Comfortable working in fast-moving, rapidly evolving environments; proactive in learning new technologies and improving existing systems 
• Pragmatic engineering mindset, with a focus on delivering high-impact solutions while maintaining long-term platform health 

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

We are looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and to help drive the direction of our ML platform.

Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction. Our ML team is full of people with a shared love for the craft of software engineering, and for designing APIs and systems that are delightful to use. 

We’ll rely on your in-depth knowledge of the ML ecosystem and understanding of varying approaches — whether it’s neural networks, random forests, gradient-boosted trees or sophisticated ensemble methods — to aid decision-making so we apply the right tool for the problem at hand. Your work will also focus on enhancing research workflows to tighten our feedback cycles. Successful ML engineers will be able to understand the mechanics behind various modelling techniques, while also being able to break down the mathematics behind them.

If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. While there isn’t a fixed list of qualifications we’re looking for, if you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in. 

We're looking for someone with:

• Experience building and maintaining training and inference infrastructure, with an understanding of what it takes to move from concept to production
• A strong mathematical background; Good candidates will be excited about things like optimisation theory, regularisation techniques, linear algebra and the like
• A passion for keeping up with the state of the art, whether that means diving into academic papers, experimenting with the latest hardware or reading the source of a new machine learning package
• A proven ability to create and maintain an organised research codebase that produces robust, reproducible results while maintaining ease of use
• Expertise wrangling an ML framework – we're fans of PyTorch, but we'd also love to learn what you know about Jax, TensorFlow or others
• An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools
• Fluent in English

If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.

We are seeking an innovative and experienced Embodied AI Researcher, focusing on the development and application of foundation models in robotics. The successful candidate will contribute to cutting-edge research, designing models and experiments that advance the field of robotics through deep learning and zero-shot generalization.

Responsibilities:

• Train foundation models for robotics, utilizing architectures similar to RT-X.
• Align text-based reasoning processes with low-level robotic control policies.
• Design and conduct experiments to evaluate zero-shot generalization in robotic systems.
• Collaborate with interdisciplinary teams to integrate AI models into practical robotic applications.
• Document and present research findings at internal and external conferences.

Qualifications:

• PhD or equivalent research experience in Deep Learning and Robotics.
• Proven track record of publications in top-tier conferences such as CoRL, ICRA, and NeurIPS.
• Practical experience with imitation learning and behavior cloning techniques.
• Strong programming skills in Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
• Excellent problem-solving skills and the ability to work independently and as part of a team.

We are seeking a highly skilled LLM Pre-training & Distributed Systems Engineer. This role is essential for orchestrating large-scale machine learning training runs and optimizing  distributed infrastructure. The ideal candidate will have a deep understanding of GPU clusters and extensive experience in system engineering to ensure efficient and reliable training processes.

Responsibilities:

• Orchestrate distributed training runs across 1,000+ GPUs using PyTorch, DeepSpeed, or Megatron-LM.
• Optimize networking (InfiniBand/RDMA) and memory management to prevent out-of-memory errors.
• Automate checkpointing and failure recovery during month-long training runs.

Required Skills:

• Deep expertise in 3D parallelism (Data, Tensor, Pipeline).
• Experience managing SLURM or Kubernetes-based GPU clusters.
• Strong systems engineering background (C++, CUDA, Python).

Capco

Capco is a leading global technology and management consultancy that excels in driving digital transformation within the financial services and insurance sectors. With an unwavering commitment to delivering practical solutions, we empower our clients to navigate the complexities of today's fast-paced market. Our integrated services span across major financial hubs worldwide, ensuring that we are always close to our clients and their needs.

At Capco, we possess not only the expertise but also the vision and passion necessary to lead transformative change. As we embark on an exciting growth journey, this is the perfect moment to join our team. We are expanding geographically, increasing our workforce, and poised to disrupt the consulting landscape across APAC with our entrepreneurial spirit and agile methodologies.

About the Role

We are thrilled to introduce an opportunity for a talented AI Engineer to join our Hong Kong team. This role is designed for a technology innovator who thrives on solving complex challenges and is passionate about building AI-driven solutions that transform the Financial Services industry. As an AI Engineer at Capco, you will play a pivotal role in shaping the future of banking and insurance by leveraging cutting-edge AI technologies to deliver intelligent, scalable, and compliant solutions. Your work will enable our clients to unlock new efficiencies, enhance customer experience, and stay ahead in a rapidly evolving digital landscape. If you are excited about applying advanced AI techniques to real-world business problems and driving measurable impact, we want you on our team.

Key Responsibilities

• Build multi-agent workflows to optimize business processes within banking and financial services.
• Develop and maintain software pipelines for data processing, model training, and deployment using Python-based frameworks.
• Train, fine-tune, and optimize neural network models, including LLMs, for specific use cases such as risk assessment and customer query resolution.
• Collaborate with data scientists, engineers, and business stakeholders to translate requirements into production-ready AI applications.
• Ensure AI models adhere to banking regulations, data privacy standards (e.g., GDPR, CCPA), and ethical AI practices.
• Conduct experiments, evaluate model performance, and iterate solutions to improve accuracy and efficiency.
• Monitor deployed solutions in production, troubleshooting issues, and implement updates as needed.
• Stay updated on emerging AI trends, particularly in NLP and LLMs, and contribute to knowledge sharing within the team.
• Participate in code reviews, agile ceremonies, and CI/CD processes.

Experience and Qualifications

• 4–10 years of professional experience in AI/ML engineering, with a focus on software development and deployment.
• Strong software engineering experience with Python and libraries such as TensorFlow, PyTorch, scikit-learn, and Huggingface.
• Demonstrated experience in NLP, including projects involving text classification, entity recognition, or similar.
• Hands-on experience training and fine-tuning neural network models, with a track record of optimizing for performance and scalability.
• Proven expertise working with LLMs (e.g., GPT, BERT) and building agentic solutions using frameworks like LangChain, LangGraph, or ADK.
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud) for model deployment and data management.
• Solid understanding of machine learning concepts, including supervised/unsupervised learning, evaluation metrics, and bias mitigation.
• Experience with version control systems (e.g., Git) and agile methodologies.
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.

Preferred Qualifications

• Experience in financial services domains such as fraud detection, credit scoring, or customer service automation.
• Familiarity with big data tools (e.g., Spark, Hadoop) and databases (SQL/NoSQL).
• Knowledge of graph databases and knowledge graphs.
• Understanding DevOps practices, containerization (Docker, Kubernetes), and MLOps workflows.
• Publications, open-source contributions, or certifications in AI/ML.
• Strong communication skills for presenting technical concepts to non-technical stakeholders.

Job Description

We are seeking a Portfolio Manager (PM) to lead our top-level capital allocation for our crypto portfolio. You will not just manage a book; you will design and execute the "Ensemble Strategy" that dictates how capital flows between strategies and emerging venues. Your mission is to engineer a "Weather-Proof" portfolio that extracts idiosyncratic alpha from disparate markets while maintaining a strictly controlled risk profile.

Responsibilities

• Dynamic Asset Allocation: Oversee the deployment of capital across the multi-strategies
• Ensemble Optimisation: Build and maintain quantitative frameworks (Risk Parity, Mean-Variance, or Bayesian models) to determine optimal weights based on real-time volatility and correlation.
• Regime-Based Hedging: Utilize Prediction Markets and FX to hedge tail risks and macro shifts impacting the core Crypto portfolios.
• Risk Budgeting: Define and monitor VaR, Stress Tests, and Drawdown limits for individual strategy sleeves.
• Cross-Asset Research: Identify "Lead-Lag" relationships—e.g., how movement in the US Dollar (DXY) or Treasury yields impacts Crypto liquidity and Commodity pricing.

Requirements

• Experience: 5-10 years in a Senior Quantitative Allocation role at a fully systematic multi-strat hedge fund, prop shop, or family office.
• Multi-Asset Mastery: Proven track record managing risk across different systematic strategies. Experience in Prediction Markets (Polymarket, Kalshi) or Event-Driven Trading is a significant plus.
• The Stack: Expert proficiency in Python (NumPy, Pandas, PyTorch/TensorFlow) and SQL/KDB+.
• Quantitative Depth: Mastery of portfolio construction mathematics, including covariance matrix estimation and L^2 regularization.
• Education: Advanced degree (Masters/PhD) in Mathematics, Physics, Computer Science, or Financial Engineering.

Who You Are

• The Aggregator: You don't just look for "good trades"; you look for how trades fit together to improve the fund's overall Information Ratio.
• The Risk-First Thinker: You understand that in a levered multi-asset environment, correlation is the silent killer.
• The Adaptable Architect: You are comfortable transitioning from the 24/7 volatility of Crypto to the structural nuances of the Commodities curve and the binary outcomes of Prediction Markets.

Location: Hong Kong or Remote
Type: Full Time

The Role

As a Software Engineer at Nex, you will contribute to building the technical foundations that power our platform's most demanding capabilities. You will focus on either ML Engineering or Framework Engineering, two complementary specializations that drive research velocity and product performance.

In the ML Engineering track, you will build the infrastructure that accelerates machine learning research: training pipelines, data workflows, model integration systems, and the tools that enable rapid experimentation. Your work ensures researchers can iterate reliably and move experiments toward production readiness.

In the Framework Engineering track, you will own the design and development of our cross-platform software framework for detection, sensing, haptics, and ML inference. You will build the real-time systems, hardware abstraction layers, and developer APIs that enable multimodal interaction on Nex Playground.

Either path offers the opportunity to work on deeply technical problems in machine learning systems, data infrastructure, sensing technologies, and real-time inference. You will be part of a small, highly technical team that values both specialization and collaboration, with clear ownership of core technology areas.

The Mindset

You are drawn to solving complex technical challenges at the intersection of research and production engineering. You care deeply about building systems that are reliable, performant, and maintainable. You thrive in environments where technical depth matters, where your expertise in ML systems, distributed computing, or real-time software directly shapes what the platform can do.

What You’ll Do

ML Engineering Track

• Design and build training pipelines, data workflows, and model integration systems
• Develop infrastructure that accelerates research iteration and reduces turnaround time
• Build systems for data collection, curation, and preprocessing at scale
• Create tools and automation that move experiments toward production readiness
• Optimize data pipelines for reliability, performance, and observability
• Collaborate with ML researchers to understand their needs and remove technical blockers
• Work on model serving infrastructure and integration with the production framework

Framework Engineering Track

• Design and develop a robust, performant, cross-platform framework for sensing, detection, haptics, and ML inference
• Build efficient runtime execution systems and hardware abstraction layers
• Develop developer APIs that make complex sensing and inference accessible
• Optimize for real-time performance on multimodal data streams
• Work on camera, microphone, and haptics integration
• Implement code that runs reliably across different platforms and hardware configurations
• Improve framework stability, performance, and developer experience

Both Tracks

• Write clean, well-tested code that maintains high engineering standards
• Participate in code reviews and help raise the engineering bar across the team
• Contribute to shared tools, infrastructure, and cross-role projects (20% Time)
• Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction
• Document systems and decisions to support team knowledge sharing

Must Have

ML Engineering Track

• 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms
• Proficiency in Python, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go)
• Hands-on experience with PyTorch or TensorFlow in production or research environments
• Experience building or maintaining ML training pipelines or data workflows
• Familiarity with model deployment, inference optimization, or MLOps practices

Framework Engineering Track

• 3+ years of professional software engineering experience in building real-time software frameworks, cross-platform systems, or performance-critical applications
• Proficiency in C++/C#, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go)
• Experience with cross-platform development
• Proven track record with performance optimization, real-time systems, or hardware abstraction layers
• Experience building developer APIs or software frameworks used by other engineers

Nice To Have

For ML Engineering Track

• Experience with distributed training systems or GPU-accelerated computing
• Knowledge of data versioning, experiment tracking, or ML metadata management
• Familiarity with containerization (Docker) and orchestration tools
• Contributions to open-source ML projects or research publications

For Framework Engineering Track

• Experience building real-time systems or low-latency software
• Familiarity with computer vision, signal processing, or embedded systems
• Experience with cross-platform development or hardware abstraction
• Knowledge of performance optimization and profiling
• Experience with sensing technologies (cameras, microphones, or similar)
• Familiarity with CI/CD pipelines and automated testing

For Either Track

• Experience working in small, high-performance technical teams
• Background in startups, high-growth environments, or consumer product companies
• Passion for pushing technical boundaries and deep problem-solving

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)

Flow Traders does not accept unsolicited resumes from any professional staffing or search firms. All resumes, and any other information identifying potential candidates, submitted to any employee at Flow Traders via-email, the Internet or directly without a valid and signed search agreement will be deemed free to contact by Flow Traders without any restrictions and no placement fee of any kind will be paid in the event the candidate is hired by Flow Traders.

Who we are

Who We Are

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. 

OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves.

Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er.

OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more.

Perks & Benefits

• Competitive total compensation package
• L&D programs and Education subsidy for employees' growth and development
• Various team building programs and company events
• Wellness and meal allowances
• Comprehensive healthcare schemes for employees and dependants
• More that we love to tell you along the process!
  
  
  Please note that Hong Kong is a group-level service hub, and OKX does not carry on a business of operating a virtual asset trading platform in Hong Kong.
  
  
  

At IMC, we believe technology is the foundation of our competitive edge — and machine learning is increasingly central to how we trade. Over the past few years, we've been steadily building our machine learning capabilities: developing infrastructure, growing our in-house GPU cluster, deploying models into production, and partnering closely with quant researchers and traders to generate real impact. Now we’re expanding the team, scaling our systems, and accelerating the application of deep learning in our research and execution workflows.  We're looking for a Principal Machine Learning Engineer to help shape the next phase of our platform — influencing architecture, driving best practices, and solving high-leverage problems. You’ll work alongside researchers and technologists to design the systems that power experimentation, training, and deployment of ML models — and help set the direction for how machine learning is done at IMC as we scale. If you’ve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firm’s future — we’d love to hear from you.

Your Core Responsibilities: 

• Design and build end-to-end infrastructure for training, evaluation, and productionization of ML models, working closely with our HPC engineers who manage our on-prem compute cluster
• Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines
• Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading
• Work with researchers to adapt and deploy modern architectures — transformers, state-space models, temporal convolutions, graph neural networks — to noisy, high-frequency financial data. Explore techniques like self-supervised pretraining, representation learning, and cross-sectional modelling where they offer genuine edge
• Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment
• Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work
• Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work — whether that's new architectures, training techniques, or tooling

Your Skills and Experience: 

• 8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm
• A track record of designing and owning large-scale training and inference systems — not just contributing, but architecting
• Deep proficiency in Python, with strong experience in either CUDA or C++
• Hands-on expertise with modern deep learning frameworks (PyTorch, TensorFlow, or JAX) and practical experience implementing architectures like transformers, attention mechanisms, or sequence models
• Strong foundation in deep learning fundamentals: optimization, regularization, loss design, and the trade-offs that matter when training at scale
• Experience with distributed training at scale (Horovod, NCCL) and GPU optimization (cuDNN, TensorRT)
• History of deploying models to production with strong observability, reproducibility, and monitoring practices
• Comfort working across the ML stack from data pipelines to training infrastructure to serving systems

Why This Role: 

• Build, don't inherit — You'll make foundational technology choices in a platform that's still being defined, not maintain someone else's legacy.
• Real investment, real backing — This is a strategic priority with resources behind it, not a side experiment.
• Direct impact on trading — Your infrastructure will power models that make real trading decisions in competitive global markets.
• Global scope — Work with teams across New York, Chicago, Amsterdam, London, Sydney, Hong Kong and beyond; define practices that can scale worldwide.
• Ideas over titles — IMC's culture values clarity, rigor, and collaboration. The best ideas win, regardless of where they come from.
• Tight coupling with research — You won't be building in isolation. Researchers and engineers work side-by-side, iterating together.

#LI-DNP

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.

This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC’s technology and trading capabilities.

Your Core Responsibilities: 

• Develop large-scale distributed training pipelines to manage datasets and complex models
• Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
• Develop libraries to improve the performance of machine learning frameworks
• Maximize performance in training and inference using GPU hardware and acceleration libraries
• Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
• Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
• Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
• Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
• Dig into the internals of open-source ML tools to extend their capabilities and improve performance

Your Skills and Experience: 

• 5+ years of experience in machine learning with a focus on training or inference systems
• Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
• Strong engineering skills, including Python, CUDA, or C++
• Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
• Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
• Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
• Exposure to cloud platforms and orchestration tools
• A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus

#LI-DNP

IMC is looking for experienced quant researchers to develop high to mid frequency delta one trading strategies and predictive models for the APAC 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, this may be the role for you.

You will be responsible for performing large scale data analysis to derive statistically profitable predictions of market behaviour. These predictions are used to inform all of our trading, and improvements have a high and visible impact across the office. You will also help to shape the direction we take across research and tooling. We have longstanding and significant edges across market access, global reach, Options understanding and low latency. The rapid growth we’ve already seen in Machine Learning has unlocked these edges, and some of the most interesting and impactful problems are now being tackled.

You will work as part of an established and growing research team, collaborating closely with traders, software and hardware developers to find improvements to our models and see them impact our production results. IMC competes and wins as a team, with open idea sharing and collaboration across disciplines, desks and offices.

Your Core Responsibilities:

• Leverage deep learning techniques to improve prediction quality in a trading context
• Collaborate with feature engineers and traders, to help ensure the feature set is crafted and used to maximum effect
• Develop and improve sampling, weighting, cost, target, hyperparameters and network architecture to suit a range of problems and data presentations
• Push the team forward, by keeping abreast of the latest developments in industry and academia, and tackling some of the hardest problems we have in the quant space
• Mentor and grow the skills of more junior colleagues in the space, and clearly and simply explain complex concepts
• Apply understanding of distributed computing, and how to align training hardware and approach to rapidly achieve strong results on very large datasets

Your Skills and Experience:

• Graduate & Postgraduate study from top universities, majoring in machine learning, statistics, or STEM subjects
• 3+ years experience as a quantitative modeller, with specific experience in the high to mid frequency delta one space. Have a proven track record of developing and improving deep learning models with proven outperformance in production
• Strong programming experience in at least one language, and mainstream deep learning framework (TensorFlow, PyTorch or other)
• Deep understanding of the strengths & weaknesses of CNN, RNN, LSTM and transformers

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models.

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*Candidates should be interested in working in Asia for their full-time job after graduation.

*INTERNATIONAL STUDENTS are encouraged to apply. 
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Who Should Apply?

• We are seeking researchers with a demonstrated ability to apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains. 
• The ideal person for this role will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure. 
• Potential projects may target any area of the quantitative research and monetization process. We believe that successful research efforts require a fluid mix of skills including ML expertise, engineering pragmatism, statistics and market intuition.
• No prior knowledge of finance or trading is necessary. We’ll give you the training that you’ll need.
  
• Other duties as assigned or needed.

Skills You'll Need: 

• Creative thinkers who are driven, self-motivated, and eager to solve challenging problems with a pragmatic outlook
• Strong publication record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent and/or contributions to open-source AI research
• Familiarity with ML libraries/frameworks such as PyTorch, TensorFlow, and/or JAX
• Proficiency in Python and/or C++
• Ability to thrive in a collaborative, team-oriented environment
• Ability to reason through quantitative problems and communicate effectively with trading researchers
• Strong general ML background with exposure to modern deep learning techniques and/or language modeling architectures (e.g. transformers, SSMs)
• Reliable and predictable availability required

If you have outstanding skills in math, ML, and programming, and you are curious about the challenge of improving research with daily feedback from competitive markets, we hope you’ll apply.

About the role 

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. 

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). 

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. 

Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

• Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). 
• Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
• Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 

2. MLOps& Data Infrastructure: 

• Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. 
• Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
• Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

3. Cross-Functional Implementation & Leadership:

• Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. 
• Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
• Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 

4. Technical Strategy & Stakeholder Management:

• Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. 
• Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
• Vector Database: Experience with Vector DBs for RAG applications. 

Why Join Us? 

• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. 
• Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. 
• Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. 
• Compensation: Competitive salary and benefits package, commensurate with experience and performance. 

Application Process 

• If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
• If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn.