401 Machine Learning Algorithms jobs in Singapore

Pluang Technologies Pte. Ltd., Singapore, Singapore, Singapore

Department

Machine Learning

Job posted on

Aug 28, 2025

Employment type

Full Time

As a Machine Learning Engineer (Trading & Financial Intelligence), you will contribute to the development of AI-powered systems and autonomous agents that transform how financial analysis and decision-making are conducted. Working under the guidance of senior team members, you will help build intelligent solutions that analyze markets, extract insights from financial data, and support risk management using machine learning and quantitative techniques. This role offers an excellent opportunity to learn and apply both traditional ML and modern LLM-based approaches to solve real financial problems while collaborating with experienced trading, research, and product teams.

What You Will Be Doing:

• Assist in designing and implementing machine learning solutions for financial markets, from predictive models to AI agents powered by LLMs
• Support the development of intelligent systems using traditional ML approaches (time series analysis, anomaly detection, pattern recognition) and modern agentic frameworks
• Help apply quantitative methods and data mining techniques to extract insights from financial datasets under senior guidance
• Contribute to building ML pipelines for model development, backtesting, and production deployment with monitoring frameworks
• Support research platforms that enable experimentation with both classical statistical models and LLM-based approaches for financial analysis
• Work closely with traders, quants, researchers, and senior engineers to understand and help solve complex financial problems
• Assist in developing risk assessment and portfolio optimization systems using quantitative methods and AI-driven approaches
• Participate in code reviews, documentation, and knowledge sharing to continuously improve technical skills

What You Need to Be Successful in This Role:

• We welcome all applicants who are eligible to work in Singapore
• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Financial Engineering, or related quantitative field
• 0-2 years of professional experience in machine learning, data science, or software engineering (internships, projects, and academic experience count)
• Solid programming skills in Python with familiarity with scientific computing libraries (pandas, numpy, scikit-learn)
• Foundational knowledge of machine learning including supervised/unsupervised learning, basic deep learning concepts, and statistical modeling
• Interest in Large Language Models and modern AI techniques - experience with prompt engineering, fine-tuning, or agentic systems is a plus but not required
• Strong mathematical and analytical foundation with ability to learn and apply quantitative concepts to practical problems
• Experience with data manipulation and basic feature engineering from structured datasets
• Eagerness to learn with ability to work collaboratively in a mentorship-oriented environment
• Good communication skills to discuss technical concepts and ask questions effectively
• Basic understanding of software engineering practices including version control (Git) and testing
• Curiosity about financial markets - prior knowledge of trading systems or quantitative finance is beneficial but not required
• Academic or personal projects demonstrating ML skills through coursework, competitions, or self-directed learning

Senior Machine Learning Engineer II

Pluang Technologies Pte. Ltd., Singapore, Singapore, Singapore

Custom Field 1: Singapore Exchange

Location:
Singapore, SG

Facility: Operations & Technology

Job Type: Contract (Project IO)

Custom Field 2: 2927

SGX is looking for a Machine Learning Engineer who is passionate about building scalable data/machine learning platforms and pioneering solutions. As a Machine Learning Engineer, you will play a crucial role in transforming how we run and deploy AI/ML models. Your work will directly impact our ability to build and deliver AI/ML use cases that will augment our users in their work or enable business opportunities. By enhancing our machine learning platform, you will enable us to make data-driven decisions and drive innovation across the organization. This is a 2-year contract role with an option for extension.

• Design and build scalable data platforms (including machine learning platforms) to support AI/GenAI use cases, streamline data storage, democratization, model deployment, and support feature engineering, model training, deployment, model monitoring and inference.
• Develop and integrate data pipelines for continuous development, integration, testing, and scalable machine learning services.
• Migrate existing data science projects to the new cloud machine learning platform.
• Collaborate with data scientists and data engineers to design, implement, and integrate data pipelines.
• Improve/automate existing model training, feature engineering, and evaluation pipelines, enabling the feedback loop of taking in labelled data from users/source systems for model re-training/tuning.
• Design and develop CI/CD pipeline, consistent logging, tracking, and monitoring of pipelines and model performance to ensure consistent model performance and alert mechanisms for model drift.
• Support data extraction and transformation for projects and users' needs.
• Review, support, improve, and run the platform, including the existing data platform/machine learning server, and environment management.
• Provide support for data-related queries, extraction, and discrepancies.
• Stay abreast of and analyze industry developments to identify areas of continuous improvement.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 2-4 years of experience in setting up a machine learning platform, developing and deploying machine learning models and AI solutions.
• Familiarity with machine learning platforms, LLM, Lang Chain.
• Proficiency in Python, SQL, and modern AI/ML algorithms, with experience in deploying solutions into production. Experience with GCP/Vertex AI is a plus.
• Highly driven, proactive, and a strong team player. Excellent interpersonal, written, and verbal communication skills in English. Ability to multitask effectively and handle large amounts of data.

Job Segment: Computer Science, Database, SQL, Learning, Technology, Human Resources

About Us

Location: Shanghai

At Qubot, we are committed to build advanced technologies to save lives. We build neuro-interventional robot to automate mechanical thrombectomy. We have a strong team of deep learning scientists, software engineers, hardware engineers and material engineers, and are currently inviting aspiring individuals to join us to create huge and meaningful impact to the medical industry in Asia.

What will I work on?

The candidate is a skilled engineer and works physics simulations, 3D volume image construction, signal processing, electromagnetic control with AI.

Our aim is to align you with the activities which truly interest you; will stretch you technically; help you grow and impact on millions of patients.

Who we love to see:

1. Strong mathematical skills - good foundations of linear algebra, matrix, differential algebra, statistics.

2. Strong physics skills - good knowledge of electromagnetism, finite element methods, and classical mechanics.

3. Strong problem solving and engineering skills.

4. Strong python, C++ or java coder

5. Good knowledge of python tools such as tensorflow, pytorch, numpy, pandas, sklearn, scipy, seaborn

6. Proficient in building deep learning neural network models

7. Optional: Published papers in top machine learning or computer vision conferences, ICML/NIPS/CVPR/ICCV

Education:

Degree in engineering or computer science or mathematics or physics

We can offer you:

Master, Teach, Learn - The opportunity to work with, learn from and share knowledge with ultra-smart colleagues in a culture of collaboration and technical excellence. We groom our employees and make sure that they stay at the cutting edge of the technology stack.

Impact - Huge potential to make a positive contribution across our business, people and partners

Remuneration – Competitive pay with share award for top performers.

We are also willing to groom engineers and fresh graduates who don't have a background in deep learning but are interested to learn. You will also have the opportunity to attend top machine learning conferences like NIPS and ICML.

Job Type: Full-time

Pay: $5, $8,000.00 per month

Work Location: In person

Arrowpoint is an Asia-focused multi-strategy hedge fund firm headquartered in Singapore, founded by former Millennium Management Asia co-CEO Jonathan Xiong. The fund launched in July 2024 with $1 billion—making it one of the largest hedge fund launches in Asia's history. Backed by prominent investors including Blackstone, the Canada Pension Plan Investment Board, and Temasek Holdings, we operate with portfolio managers across Singapore and Hong Kong, integrating diverse strategies such as Equities, Fixed Income, and Commodities.As we continue to grow, we are seeking driven individuals to join our team and contribute to our mission.

We are on the lookout for a Junior Machine Learning Engineer who will play a key role in enhancing our Machine Learning capabilities, focusing on innovative solutions for systematic strategies. This role is not just about technical expertise but also about aligning with our forward-thinking vision and being at the forefront of shaping our AI strategy.

Key Responsibilities

ML Engineering and Operations:

Develop, implement and optimize predictive models using machine learning and deep learning / AI techniques.

Deploy, maintain and monitor production models on AWS.

ETL & Data Management:

Design and develop efficient ETL pipelines to process and manage large volumes of financial data and alternative data.

Ensure data integrity and accuracy across various data sources.

Cloud Computing:

Utilize Cloud services to deploy and manage machine learning models in a scalable and reliable manner (Terraform, AWS ECS, Docker, CI/CD pipelines).

Optimize Cloud resources for performance and cost-effectiveness.

End-to-End Solution Development:

Engage in the complete lifecycle of model development, from data collection and preprocessing to model deployment and monitoring.

Explore and implement innovative techniques to solve complex financial problems.

Stay current with the latest advancements in machine learning and data science to continually improve and innovate.

Result-Oriented Approach:

Focus on delivering high-impact results.

Communicate findings and recommendations effectively to senior stakeholders.

Requirements & Qualifications:

Bachelor's or Master's degree in Computer Science, Machine Learning, AI, Statistics, Mathematics, or a related field.

1-3 years of hands-on experience in data science and machine learning (exposure to financial markets in a professional context in a plus).

Proficiency in Python and relevant libraries (e.g., scikit-learn, Polars, PyTorch).

Experience with AWS services (e.g., S3, ECS, Lambda, Athena, CloudWatch, etc.).

Strong understanding of Data platform ETL processes and tools.

Excellent problem-solving skills and a creative mindset.

Strong communication and collaboration skills.

This website contains important legal and proprietary information concerning Arrowpoint Investment Partners (Singapore) Pte. Ltd. and its affiliates ("Arrowpoint"). The content of this website is intended for information purposes only and is not intended as advertising or promotional material in any respect. The information contained in this website is not and should not be regarded as an offer or solicitation of business, nor should it be construed as an offer, solicitation, invitation, or recommendation to buy or sell any securities, funds, or other investment products. Nothing in this website constitutes an offer or promise of employment with Arrowpoint with respect to any role described herein. Arrowpoint reserves the right, without notice, to remove, change or modify the terms of any role described herein. Any offer of employment with Arrowpoint shall be solely in accordance with the specific terms of such offer of employment. While Arrowpoint takes reasonable care to ensure that the information on this website is accurate and up to date, Arrowpoint assumes no obligation to update such information on a timely basis. Arrowpoint Investment Partners (Singapore) Pte. Ltd. is licenced by the Monetary Authority of Singapore to conduct the regulated activity of fund management. Arrowpoint Investment Partners (Hong Kong) Limited is licenced by the Securities and Futures Commission of Hong Kong to carry on the regulated activity of asset management.

Support Project/System implementation of Machine Learning Suite (MLS)

Ensure solution design complies with enterprise design principles, security and control standards

Convert business requirements to design document and other system documentation to capture key design decisions

Able to conduct data mapping and process mapping with interfacing applications

Create technical documents for the solutions

Conduct root cause analysis of issues, review new and existing code and/or perform unit testing

Work with Test Management teams to complete SIT/UAT as planned

Manage project change request approval windows and deployment schedule

Facilitate and provide technical and testing support before and after production deployment

Manage external vendors for project delivery within schedule

a, Experience end-to-end projects for system implementation of Machine Learning Suite (MLS)

b. Strong in driving project deliverables, actively tracking timelines, escalating issues when needed, and ensuring accountability across teams.

c. Strong in Project Management, Financial Management, Application Management, Risk and Issue Management, Change Management and Resource Management

d. experience in application architecture, development, system integration and software design

e.Experience in Production Support Management, Incident and Problem Management

f. Knowledge in networking, firewall, data migration, password encryption, SSO integration, and disaster recovery planning and testing

Job Type: Contract

Contract length: 12 months

Benefits:

• Health insurance

Work Location: In person

• Develop and optimize algorithms for physiological signal analysis, action classification, and recognition in wearable devices.
• Build and implement hardware platforms to ensure algorithm accuracy, reliability, and scalability.
• Apply cutting-edge algorithms to enhance real-world products, delivering superior user experiences.

• Degree in Electronic Engineering, Biomedical Engineering, Microelectronics, Automation, or related disciplines.
• A curious, research-driven mindset with a passion for scientific discovery and innovation.
• Strong logical thinking and ability to adapt quickly to the latest advancements in science.
• Enthusiasm for transforming research into real-world solutions with global impact.
• Bonus points for experience in signal processing, modeling competitions, or acoustic projects.

• Agentic AI Development: Design, develop, and implement sophisticated AI agents and bots capable of independent operation, complex decision-making, and self-correction within various domain-specific contexts.
  Utilize and seamlessly integrate with advanced AI orchestration frameworks such as LangChain and LangGraph , and potentially the Microsoft Bot Framework , to build robust conversational and task-oriented agents.
  Work with state-of-the-art large language model (LLM) serving solutions like Ollama and vLLM to ensure efficient and scalable inference for our AI agents.
  Implement Retrieval-Augmented Generation (RAG) systems to enable agents to access, synthesize, and generate responses based on external, up-to-date knowledge bases, mitigating hallucinations and ensuring factual accuracy.
  Develop and integrate Memory, Reasoning, and Planning (MRP) capabilities within agents, allowing them to maintain context, reason over information, formulate multi-step plans, and adapt to dynamic environments.
  Design and implement Agent-to-Agent (A2A) communication protocols , enabling seamless and secure collaboration, task delegation, and information exchange between different autonomous agents, regardless of their underlying frameworks or platforms.
  Work across diverse data modalities including image, time-series, text, and graph data , developing models and agents that can interpret, process, and generate insights from heterogeneous data sources.
• Deployment and MLOps: Strategically deploy and manage AI agents and systems on leading cloud environments, including AWS, Azure, or Google Cloud Platform (GCP) , ensuring high availability, scalability, and security.
  Expose AI functionalities through well-documented and performant APIs, enabling seamless integration with client systems and applications.
  Handle on-premise deployments, which includes the end-to-end setup, configuration, and maintenance of Kubernetes clusters for containerized applications.
  Implement robust containerization strategies (e.g., Docker) and establish efficient orchestration workflows using tools like Argo to automate deployment, scaling, and management of AI services.
  Establish and maintain CI/CD pipelines for AI models and applications, ensuring rapid iteration and reliable delivery.
• Model Training & Fine-tuning: Leverage expertise in deep learning frameworks (e.g., TensorFlow, PyTorch) to train, fine-tune, and optimize custom AI models across various data types. This will be crucial where off-the-shelf models don't meet specific project requirements, ensuring peak performance and accuracy.
  Conduct experimentation, hyperparameter tuning, and model evaluation to achieve optimal model performance.
  Stay abreast of the latest research and advancements in AI/ML, particularly in the areas of large language models and agentic AI, to continuously improve our systems.

• Proven, hands-on experience in the end-to-end development and deployment of AI agents and autonomous systems in production environments.
• Strong proficiency with AI orchestration frameworks such as LangChain, LangGraph , and/or experience with the Microsoft Bot Framework .
• Practical experience with LLM serving technologies such as Ollama or vLLM .
• Demonstrable experience implementing and optimizing Retrieval-Augmented Generation (RAG) systems for enhanced AI outputs.
• Understanding and practical application of Memory, Reasoning, and Planning (MRP) concepts in agentic AI development.
• Familiarity with and/or experience implementing Agent-to-Agent (A2A) communication protocols .
• Experience working with and processing diverse data modalities, including image processing, time-series analysis, natural language processing (NLP), and graph data structures.
• Extensive hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) for deploying and managing AI solutions, including familiarity with relevant services (e.g., EC2, S3, Azure ML, GCP AI Platform).
• In-depth knowledge of containerization technologies (e.g., Docker) and substantial experience with container orchestration using Kubernetes .
• Solid understanding and practical application of MLOps practices and tools , with specific familiarity in orchestration tools like Argo (Argo Workflows, Argo CD) .
• Proficiency in modern deep learning frameworks (e.g., TensorFlow, PyTorch) for model training, fine-tuning, and evaluation.
• Strong programming skills in Python, coupled with experience in version control systems like Git.
• Excellent problem-solving abilities, a strong analytical mindset, and the capacity to thrive in a fast-paced, startup environment.

• Experience with knowledge graphs or semantic web technologies for advanced knowledge representation.
• Understanding of prompt engineering and fine-tuning strategies for large language models.
• Familiarity with data governance, security, and privacy best practices in AI deployments.
• Contributions to open-source AI projects or relevant publications.
• Patents and Publications in relevant fields.

Unlock Opportunities in Tribology and Data Science

We are seeking a highly skilled Machine Learning Engineer to join our team. As a key member of our data science group, you will apply cutting-edge machine learning techniques to model, simulate, and optimize the behavior of lubricants, dispersants, and related materials.

Your primary responsibility will be to collaborate with R&D, Engineering, and Product teams to uncover insights from experimental, simulation, and operational datasets. You will develop, validate, and deploy predictive models using Azure Machine Learning, supporting product development and process optimization.

• Apply machine learning and statistical modeling techniques to identify trends, anomalies, and correlations in large-scale, multi-source datasets.
• Collaborate with cross-functional teams to develop and maintain pipelines and models for continuous learning and performance monitoring in production environments.
• Contribute to the development of scientific publications, patents, or internal knowledge bases.
• Support data governance, data quality, and reproducibility of scientific modeling efforts.

• MSc or PhD in Tribology, Chemical Engineering, Materials Science, Mechanical Engineering, or a related field with strong machine learning exposure.
• Solid understanding of lubricants, dispersants, and their chemical/physical mechanisms.
• 3+ years of experience in machine learning, data science, or computational modeling roles.
• Proficiency in Python and key machine learning libraries (e.g., scikit-learn, pandas, NumPy, matplotlib).
• Hands-on experience with Azure Machine Learning (AML) - model training, deployment, versioning, and monitoring.
• Familiarity with statistical modeling, optimization, and time-series or multivariate analysis.

• Experience working with sensor data, lab data, or simulation data in a tribological or chemical context.
• Knowledge of physics-informed machine learning, hybrid modeling, or surrogate modeling techniques.
• Familiarity with tools like MLflow, Databricks, Azure Data Factory, or Power BI.