2,461 AI Programmer jobs in Singapore

About Innowave Tech Singapore
Innowave Tech is an Artificial Intelligence (AI) company offering solutions for the Semiconductor and Advanced Manufacturing industry. Utilizing deep industrial domain knowledge, proven experience, and innovation, we provide expert AI solutions and systems to address various industry pain points.
Roles & Responsibilities
We are seeking a Machine Learning Software Engineer to transform data science solutions into production-ready systems. The successful candidate will be responsible for productionizing algorithms and models efficiently for our manufacturing analytics solutions.
Your Role and Impact
As our ML Software Engineer, you will have a crucial role in bridging the gap between data science research and production systems. You will work closely with Data Scientists to transform algorithms and AI/ML models prototypes into production systems. Your role will be focusing on robust software architecture, efficient processing systems, and scalable deployment solutions. You will work alongside Data Engineers to ensure seamless integration of ML systems with data infrastructure.
What You’ll Do
Transform experimental feature extraction algorithms and ML models from prototypes created by data scientists into production-ready systems.
Build efficient data processing pipelines for model training and inference.
Develop backend services for real-time predictions and analytics.
Optimize database queries and data access patterns.
Create automated testing and validation systems.
What We’re Looking For
Educational Background:
Minimum Poly or Bachelor Degree in Computer Science, Engineering, or a related field.
Technical Expertise:
3+ years of software engineering experience, with 1+ year in ML systems.
Strong Python programming and software engineering practices.
Strong system design and software architecture thinking.
Proficiency in SQL database, NoSQL database, and in-memory caching (e.g., Redis).
Experience with ML frameworks and model serving, with solid understanding of ML pipelines and MLOps practices.
Experience with RESTful API, task queue system (e.g., Celery), and data pipeline orchestrator (e.g., Apache Airflow, Dagster).
Experience with containerization (Docker), version control (Git), and CI/CD practices.
Solid understanding in software testing practices (unit testing, integration testing).
Knowledge of performance optimization and profiling tools.
Soft Skills:
Excellent communication skills and ability to collaborate with cross-functional technical and non-technical teams.
Excellent problem-solving and debugging abilities.
Ability to balance engineering tradeoffs.
Bonus Skills:
Experience with distributed computing systems.
Familiarity with frontend web development.
Knowledge of time-series data processing and signal processing techniques.
What we Offer

• A leading role in cutting-edge AI projects within the semiconductor industry.
• The opportunity to work with an learn from experts in the field of AI and data science.
• A dynamic, innovative, and supportive work environment.
• Competitive salary and benefits package.
• Career growth opportunities in a fast-paces technology company.
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Meta is seeking talented engineers to join our teams in building cutting-edge products that connect billions of people around the world. As a member of our team, you will have the opportunity to work on complex technical problems, build new features, and improve existing products across various platforms, including mobile devices and web applications. Our teams are constantly pushing the boundaries of user experience, and we're looking for passionate individuals who can help us advance the way people connect globally. If you're interested in joining a world-class team of engineers and researchers to work on exciting projects that have significant impact, we encourage you to apply.

• Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences
• Implement custom user interfaces using latest programming techniques and technologies
• Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
• Set direction and goals for teams, lead major initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
• Architect efficient and scalable systems that drive complex applications
• Identify and resolve performance and scalability issues, and drive large efforts to reduce technical debt
• Work on a variety of coding languages and technologies
• Establish ownership of components, features, or systems with expert end-to-end understanding

• 8+ years of programming experience in a relevant language OR 4+ years experience with a PhD
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Demonstrated experience driving change within an organization and leading complex technical projects
• Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions

• Masters degree or PhD in Computer Science or a related technical field
• Experience with frameworks like TensorFlow, PyTorch, or Scikit-learn
• Knowledge of NLP techniques, including text preprocessing, tokenization, and sentiment analysis
• Understanding of information retrieval concepts, such as indexing, querying, and ranking
• Grasp of data structures and algorithms, including graph theory and optimization techniques

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

As a Software Engineer (Machine Learning), you will develop and leverage innovative machine learning solutions for solving complex problems and applications. By analyzing and detecting patterns in vast amounts of data, you should have a good understanding of the machine learning lifecycle, algorithms, data structures, and design patterns.
Responsibilities
Design and develop machine learning algorithms.
Discover, design, and develop analytical methods to support novel data and information processing approaches.
Identify and apply appropriate methods to process and analyze large datasets of labeled and unlabeled records, discovering valuable insights.
Support other parts of the system beyond machine learning.
Review and improve current software and system architecture for stability and performance optimization.
Research and develop fraud detection solutions.
Minimum Qualifications
Bachelor's Degree in Computer Science, Information Systems with a specialization in Machine Learning or equivalent.
Strong foundation in databases and data scaling.
Experience with various machine learning algorithms and their application in real-world cases.
Experience with MySQL, NoSQL, and Columnar databases.
Proficiency in C++, C, Python, and other programming languages is an advantage.
Prior experience in e-payments or e-commerce industry is a plus.
Strong analytical, interpersonal, communication, and presentation skills.
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About Airwallex
Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 150,000 businesses worldwide – including Brex, Rippling, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.
Proudly founded in Melbourne, we have a team of over 1,700 of the brightest and most innovative people in tech across more than 37 locations around the globe. Valued at US$5.6 billion and backed by world-leading investors including Sequoia, Lone Pine, Greenoaks, DST Global, Salesforce Ventures and Mastercard, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.
About the team
The Risk Platform Team at Airwallex is responsible for managing the risk for all the products at Airwallex, including GTPN, PA, Issuing, Onboarding, and Account takeover. The risk landscape is constantly changing, and fraudsters are becoming increasingly sophisticated. We are at the forefront of innovation in risk management. Our team builds and maintains the robust, scalable infrastructure that powers our advanced machine learning models, enabling rapid iteration and deployment of risk strategies.
What you’ll do
Our mission is to keep Airwallex's products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. You will be instrumental in designing, building, and optimizing the end-to-end machine learning platform that enables rapid development, deployment, and monitoring of ML models. We leverage cutting-edge technologies, including big data frameworks, real-time streaming, MLOps best practices, graph technologies, and Large Language Models (LLMs), to implement and improve our strategy.
Our team expands across Beijing, Shanghai and Singapore. We collaborate with other teams (Data Science, Product, Engineering) and our customers globally to ensure a holistic approach for risk management and deliver state-of-the-art ML capabilities.
Responsibilities:
Design, build, and maintain scalable and reliable data pipelines for ingesting, processing, and transforming large datasets (batch and stream) for ML model training and inference.
Develop and manage feature generation systems and feature stores, ensuring data quality, consistency, reusability, and accessibility for model development.
Architect and implement robust model serving infrastructure for deploying, managing, and monitoring machine learning models in production at scale, ensuring low latency and high availability.
Collaborate with Data Scientists, ML Engineers, and Senior Technical Staff to understand ML requirements and translate them into robust platform capabilities and infrastructure.
Champion and implement MLOps best practices, including CI/CD for ML, model versioning, experiment tracking, automated monitoring, and feedback loops.
Experiment with new technologies and frameworks in the big data, streaming, and MLOps space, proposing architectural improvements for the ML platform.
Support and mentor less-experienced team members in ML platform engineering, big data technologies, data pipeline development, and MLOps practices.
Ensure the security, scalability, performance, and cost-effectiveness of the ML platform components.
Who you are
We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.
Minimum qualifications:
More than 5 years of software engineering experience, with at least 3+ years focused on building and maintaining machine learning platforms, big data systems, or large-scale data infrastructure.
Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
Proven experience with big data technologies (e.g., Apache Spark, Hadoop, Presto, Hive, Flink) and distributed computing.
Strong proficiency in at least one programming language commonly used in data engineering and ML (e.g., Python, Scala, Java).
Experience in designing, implementing, and managing complex ETL/ELT data pipelines and workflow management tools (e.g., Apache Airflow, Kubeflow Pipelines, Dagster).
Hands-on experience with real-time data streaming technologies (e.g., Apache Kafka, Flink, Spark Streaming) and building stream processing applications.
Experience with designing and implementing solutions for feature generation, feature engineering at scale, and/or feature stores.
Familiarity with model serving patterns, tools, and infrastructure (e.g., KFServing, Seldon Core, BentoML, Triton Inference Server, or custom API development for model deployment).
Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
Proficient with build tools (e.g., Gradle, Maven, SBT) and version control systems (e.g., Git).
Preferred qualifications:
In-depth knowledge of MLOps principles and hands-on experience implementing MLOps pipelines and tools for continuous integration, continuous delivery, and continuous training (CI/CD/CT).
Experience with cloud platforms (e.g., Google Cloud, AWS, Azure) and their ML/data services (e.g., Vertex AI, SageMaker, Databricks, EMR).
Familiarity with various database technologies (e.g., NoSQL, SQL, Graph Databases) and their application in ML systems.
Knowledge of infrastructure-as-code tools (e.g., Terraform, Ansible).
Understanding of machine learning algorithms, model lifecycle management, and evaluation metrics.
Experience building platforms for risk management, fraud detection, or anomaly detection, particularly within financial and fintech industries.
Strong analytical and problem-solving skills, with the ability to tackle complex technical challenges.
Excellent communication and collaboration skills, 1 with experience working in agile environments.
Equal opportunity
Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.
Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.
#J-18808-Ljbffr

About Airwallex
Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 150,000 businesses worldwide – including Brex, Rippling, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.
Proudly founded in Melbourne, we have a team of over 1,700 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$6.2 billion and backed by world-leading investors including Visa, Airtree, Blackbird, Sequoia, DST Global, Greenoaks, Salesforce Ventures, Lone Pine, and Square Peg, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.
About the team
The Risk Platform Team at Airwallex is responsible for managing the risk for all the products at Airwallex, including GTPN, PA, Issuing, Onboarding, and Account takeover. The risk landscape is constantly changing, and fraudsters are becoming increasingly sophisticated. We are at the forefront of innovation in risk management. Our team builds and maintains the robust, scalable infrastructure that powers our advanced machine learning models, enabling rapid iteration and deployment of risk strategies.
What you’ll do
Our mission is to keep Airwallex's products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. You will be instrumental in designing, building, and optimizing the end-to-end machine learning platform that enables rapid development, deployment, and monitoring of ML models. We leverage cutting-edge technologies, including big data frameworks, real-time streaming, MLOps best practices, graph technologies, and Large Language Models (LLMs), to implement and improve our strategy.
Our team expands across Beijing, Shanghai and Singapore. We collaborate with other teams (Data Science, Product, Engineering) and our customers globally to ensure a holistic approach for risk management and deliver state-of-the-art ML capabilities.
Responsibilities:
Design, build, and maintain scalable and reliable data pipelines for ingesting, processing, and transforming large datasets (batch and stream) for ML model training and inference.
Develop and manage feature generation systems and feature stores, ensuring data quality, consistency, reusability, and accessibility for model development.
Architect and implement robust model serving infrastructure for deploying, managing, and monitoring machine learning models in production at scale, ensuring low latency and high availability.
Collaborate with Data Scientists, ML Engineers, and Senior Technical Staff to understand ML requirements and translate them into robust platform capabilities and infrastructure.
Champion and implement MLOps best practices, including CI/CD for ML, model versioning, experiment tracking, automated monitoring, and feedback loops.
Experiment with new technologies and frameworks in the big data, streaming, and MLOps space, proposing architectural improvements for the ML platform.
Support and mentor less-experienced team members in ML platform engineering, big data technologies, data pipeline development, and MLOps practices.
Ensure the security, scalability, performance, and cost-effectiveness of the ML platform components.
Who you are
We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.
Minimum qualifications:
More than 3 years of software engineering experience, with at least 1+ years focused on building and maintaining machine learning platforms, big data systems, or large-scale data infrastructure.
Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
Proven experience with big data technologies (e.g., Apache Spark, Hadoop, Presto, Hive, Flink) and distributed computing.
Strong proficiency in at least one programming language commonly used in data engineering and ML (e.g., Python, Scala, Java).
Experience in designing, implementing, and managing complex ETL/ELT data pipelines and workflow management tools (e.g., Apache Airflow, Kubeflow Pipelines, Dagster).
Hands-on experience with real-time data streaming technologies (e.g., Apache Kafka, Flink, Spark Streaming) and building stream processing applications.
Experience with designing and implementing solutions for feature generation, feature engineering at scale, and/or feature stores.
Familiarity with model serving patterns, tools, and infrastructure (e.g., KFServing, Seldon Core, BentoML, Triton Inference Server, or custom API development for model deployment).
Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
Proficient with build tools (e.g., Gradle, Maven, SBT) and version control systems (e.g., Git).
Preferred qualifications:
In-depth knowledge of MLOps principles and hands-on experience implementing MLOps pipelines and tools for continuous integration, continuous delivery, and continuous training (CI/CD/CT).
Experience with cloud platforms (e.g., Google Cloud, AWS, Azure) and their ML/data services (e.g., Vertex AI, SageMaker, Databricks, EMR).
Familiarity with various database technologies (e.g., NoSQL, SQL, Graph Databases) and their application in ML systems.
Knowledge of infrastructure-as-code tools (e.g., Terraform, Ansible).
Understanding of machine learning algorithms, model lifecycle management, and evaluation metrics.
Experience building platforms for risk management, fraud detection, or anomaly detection, particularly within financial and fintech industries.
Strong analytical and problem-solving skills, with the ability to tackle complex technical challenges.
Excellent communication and collaboration skills, 1 with experience working in agile environments.
Equal opportunity
Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.
Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.
#J-18808-Ljbffr

About the team

The Risk Platform Team at Airwallex is responsible for managing the risk for all the products at Airwallex, including GTPN, PA, Issuing, Onboarding, and Account takeover. The risk landscape is constantly changing, and fraudsters are becoming increasingly sophisticated. We are at the forefront of innovation in risk management. Our team builds and maintains the robust, scalable infrastructure that powers our advanced machine learning models, enabling rapid iteration and deployment of risk strategies.

What you’ll do

Our mission is to keep Airwallex's products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. You will be instrumental in designing, building, and optimizing the end-to-end machine learning platform that enables rapid development, deployment, and monitoring of ML models. We leverage cutting-edge technologies, including big data frameworks, real-time streaming, MLOps best practices, graph technologies, and Large Language Models (LLMs), to implement and improve our strategy.

Our team expands across Beijing, Shanghai and Singapore. We collaborate with other teams (Data Science, Product, Engineering) and our customers globally to ensure a holistic approach for risk management and deliver state-of-the-art ML capabilities.

Responsibilities:

1. Design, build, and maintain scalable and reliable data pipelines for ingesting, processing, and transforming large datasets (batch and stream) for ML model training and inference.
2. Develop and manage feature generation systems and feature stores, ensuring data quality, consistency, reusability, and accessibility for model development.
3. Architect and implement robust model serving infrastructure for deploying, managing, and monitoring machine learning models in production at scale, ensuring low latency and high availability.
4. Collaborate with Data Scientists, ML Engineers, and Senior Technical Staff to understand ML requirements and translate them into robust platform capabilities and infrastructure.
5. Champion and implement MLOps best practices, including CI/CD for ML, model versioning, experiment tracking, automated monitoring, and feedback loops.
6. Experiment with new technologies and frameworks in the big data, streaming, and MLOps space, proposing architectural improvements for the ML platform.
7. Support and mentor less-experienced team members in ML platform engineering, big data technologies, data pipeline development, and MLOps practices.
8. Ensure the security, scalability, performance, and cost-effectiveness of the ML platform components.

Who you are We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.

Minimum qualifications:

1. More than 3 years of software engineering experience, with at least 1+ years focused on building and maintaining machine learning platforms, big data systems, or large-scale data infrastructure.
2. Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
3. Proven experience with big data technologies (e.g., Apache Spark, Hadoop, Presto, Hive, Flink) and distributed computing.
4. Strong proficiency in at least one programming language commonly used in data engineering and ML (e.g., Python, Scala, Java).
5. Experience in designing, implementing, and managing complex ETL/ELT data pipelines and workflow management tools (e.g., Apache Airflow, Kubeflow Pipelines, Dagster).
6. Hands-on experience with real-time data streaming technologies (e.g., Apache Kafka, Flink, Spark Streaming) and building stream processing applications.
7. Experience with designing and implementing solutions for feature generation, feature engineering at scale, and/or feature stores.
8. Familiarity with model serving patterns, tools, and infrastructure (e.g., KFServing, Seldon Core, BentoML, Triton Inference Server, or custom API development for model deployment).
9. Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
10. Proficient with build tools (e.g., Gradle, Maven, SBT) and version control systems (e.g., Git).

Preferred qualifications:

1. In-depth knowledge of MLOps principles and hands-on experience implementing MLOps pipelines and tools for continuous integration, continuous delivery, and continuous training (CI/CD/CT).
2. Experience with cloud platforms (e.g., Google Cloud, AWS, Azure) and their ML/data services (e.g., Vertex AI, SageMaker, Databricks, EMR).
3. Familiarity with various database technologies (e.g., NoSQL, SQL, Graph Databases) and their application in ML systems.
4. Knowledge of infrastructure-as-code tools (e.g., Terraform, Ansible).
5. Understanding of machine learning algorithms, model lifecycle management, and evaluation metrics.
6. Experience building platforms for risk management, fraud detection, or anomaly detection, particularly within financial and fintech industries.
7. Strong analytical and problem-solving skills, with the ability to tackle complex technical challenges.
8. Excellent communication and collaboration skills, 1 with experience working in agile environments.

Job Responsibilities:

• Develop enterprise-level Java applications used by over 200 banks and financial institutions for authentication, authorization, and single sign-on.
• Build and maintain front-end interfaces with ReactJS, AngularJS, and Google Web Toolkit.
• Analyze business requirements and design software architecture using design patterns to ensure scalability and ease of maintenance.
• Investigate and resolve complex issues that Level 1 and 2 support teams cannot address, offering recommendations to identify root causes.
• Write clean, well-documented code that adheres to industry best practices.

Job Requirements:

• Degree in Computer Science or Engineering with relevant programming experience.
• 5 to 10 years of experience in a similar role.
• Strong analytical and problem-solving skills.
• Excellent communication skills for effective collaboration with developers, QA teams, team leads, and stakeholders.
• Proficiency in Java and at least one or more mainstream programming languages like TypeScript/JavaScript, C/C++, or C#.
• Expertise in Tomcat, Web Services, REST APIs, Docker, and Kubernetes/OpenShift.
• Experience with databases such as MySQL, Oracle, SQL Server, or Redis.
• Knowledge of software architecture, design patterns, and best coding practices.
• Ability to work independently and collaboratively in a team environment.
• Willingness to learn and adapt to new technologies and skills.

Apply Now

Join us and enhance your software engineering skills in a supportive learning environment. You'll gain expertise in software architecture, design patterns, best coding practices, and proper documentation.

As a Software Engineer with a strong attention to detail, you would be passionate about software development and delivering excellence. You should be able to focus on specific requirements while also considering end-to-end project strategies. You would work with all project stakeholders.

Key Responsibilities:

• Experience working in Java/.NET Core with API development and SQL databases.
• Minimum 7+ years of experience as a Frontend/Backend developer with strong object-oriented programming skills.
• Experience with Unit Testing frameworks like nUnit, xUnit, or MsTest, and good knowledge of various test cases including automation.
• Produce quality code adhering to performance, readability, and reusability best practices.
• Optimize applications for maximum speed and scalability.
• Implement Sitecore SXA and Habitat Components, integrating with external systems, including database design, data access, and ORMs, would be a plus.
• Develop scalable, cloud-based solutions with consideration for security and performance.