118 Ml Engineer jobs in Malaysia

Join to apply for the AI/ML Engineer role at Horizontal Talent

Join to apply for the AI/ML Engineer role at Horizontal Talent

Job Title: AI/ML Engineer

Work Arrangement: Hybrid

Location: Kuala Lumpur, Malaysia

About Horizontal: Established since 2003 in the US, Horizontal solves complex challenges across two distinct businesses: Horizontal Digital and Horizontal Talent. We are consistently recognized for being a top workplace and one of the fastest growing private companies. Horizontal Talent specializes in staffing for IT, Digital & Creative and Business & Strategy markets. We have global offices in US, UAE, India, Malaysia and Australia.

Our Client is seeking a talented and exceptional Machine Learning Engineer who wants to make a difference in the world, has a strong drive for continuous learning and development, is open and collaborative, and never stops striving to improve themselves and the products or services they are responsible for. You will be responsible for developing and optimizing advanced machine learning (ML) models across various domains, including natural language processing (NLP), computer vision, and speech processing. This role requires broad expertise in large language models and diffusion models, with experience in ASR systems to support our comprehensive data analytics platform.

Key Responsibilities

• Design, implement, and fine-tune Large Language Models (LLMs) for various NLP tasks, including text classification, entity extraction, sentiment analysis, and conversational AI applications.
• Develop and optimize diffusion models for generating synthetic data, augmenting data, and creating multimodal content to enhance training datasets.
• Build and maintain end-to-end machine learning pipelines for text processing, speech recognition, and multimodal data analysis.
• Implement state-of-the-art transformer architectures and attention mechanisms for various sequence modeling tasks across different data modalities.
• Research and integrate cutting-edge ML techniques, including few-shot learning, transfer learning, and multi-task learning approaches.
• Collaborate with engineering teams to deploy ML models in production environments, ensuring scalability and reliability.
• Develop custom training strategies, loss functions, and evaluation frameworks for domain-specific applications in telecommunications and government sectors.
• Optimize model performance, inference speed, and resource utilization for deployment across various hardware configurations.
• Conduct comprehensive model evaluation, A/B testing, and performance monitoring to ensure continuous improvement.
• Create technical documentation, research reports, and best practice guidelines for ML development processes.
• Mentor junior team members and contribute to the advancement of ML practices across the organization.
  
  

• Advanced degree in Computer Science, Machine Learning, Data Science, Mathematics, or a related field.
• 4+ years of experience in machine learning engineering with a focus on deep learning and neural networks.
• Strong proficiency in Python and extensive experience with PyTorch, TensorFlow, and the HuggingFace ecosystem.
• Demonstrated expertise with Large Language Models including GPT, BERT, T5, LLaMA, and similar transformer-based architectures.
• Hands-on experience with diffusion models such as Stable Diffusion, DDPM, DDIM, or similar generative modeling techniques.
• Experience with ASR (Automatic Speech Recognition) systems and speech processing pipelines, including models like Whisper, Wav2Vec2, or similar technologies.
• Solid understanding of natural language processing techniques, including tokenization, embeddings, attention mechanisms, and sequence modeling.
• Experience with parameter-efficient fine-tuning methods, including LoRA, QLoRA, AdaLoRA, and other PEFT techniques.
• Knowledge of distributed training, model parallelism, and large-scale data processing frameworks.
• Familiarity with MLOps practices, model deployment, and production monitoring systems.
• Strong mathematical foundation in statistics, linear algebra, and optimization theory.
  
  

• Programming Languages: Expert-level Python; proficiency in C/C++, JavaScript, or Julia for performance optimization.
• Deep Learning Frameworks: Advanced experience with PyTorch, TensorFlow, JAX, and HuggingFace Transformers.
• LLM Technologies: Extensive knowledge of transformer architectures, attention mechanisms, tokenizers, and model scaling techniques.
• Generative Models: Hands-on experience with diffusion models, VAEs, GANs, and other generative modeling approaches.
• Fine-Tuning Techniques: Proficiency with LoRA, QLoRA, prefix tuning, prompt tuning, adapters, and instruction tuning methodologies.
• NLP Libraries: Experience with spaCy, NLTK, Gensim, sentence-transformers, and domain-specific NLP tools.
• Speech Processing: Familiarity with librosa, torchaudio, SpeechBrain, and audio processing pipelines.
• Model Optimization: Knowledge of quantization, pruning, distillation, ONNX, TensorRT, and inference optimization.
• Data Processing: Expertise in pandas, NumPy, Apache Spark, Dask, and distributed computing frameworks.
• MLOps Tools: Experience with Docker, Kubernetes, MLflow, Weights & Biases, Ray, and cloud ML platforms.
• Proficient in version control using Git, DVC, and collaborative ML development workflows.
  
  

• Experience with multimodal learning combining text, audio, and visual data modalities.
• Knowledge of reinforcement learning from human feedback (RLHF) and constitutional AI approaches.
• Familiarity with federated learning and privacy-preserving machine learning techniques.
• Experience with real-time inference systems and streaming data processing.
• Understanding of model interpretability, explainable AI, and bias detection methodologies.
• Knowledge of graph neural networks and knowledge graph embedding techniques.
• Experience with AutoML frameworks and neural architecture search (NAS).
• Contributions to open-source ML projects or published research in top-tier conferences.
• Experience with edge deployment and mobile optimization for ML models.
• Understanding of cybersecurity applications and adversarial machine learning.
  
  

• Exceptional problem-solving skills and ability to approach complex ML challenges from first principles.
• Strong research mindset with the ability to stay current with the rapidly evolving ML/AI landscape.
• Excellent communication skills, both written and verbal, with the ability to explain complex concepts to diverse audiences.
• Self-driven and proactive, with a passion for pushing the boundaries of machine learning technology.
• Adaptable and open-minded, able to work effectively across different problem domains and data modalities.
• Collaborative team player with experience working in cross-functional environments.
• Strong attention to detail with commitment to reproducible and ethical AI practices.
  
  

• Language: Fluent oral and written English is mandatory.
• Good interpersonal skills
• Excellent organizational skills
• Team player but able to work on own initiative
• Good written and oral communication skills
• Enthusiastic, self-starter and highly self-motivated
• Appreciation of cultural differences
• Attention to detail
• Travel: Occasional travel may be required for conferences, client meetings, or inter-office collaboration.

• Seniority level Not Applicable

• Employment type Full-time

• Job function Engineering and Information Technology
• Industries Staffing and Recruiting

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We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

As an AI & Machine Learning Engineer, your responsibility will include proposing and developing innovative solutions for our business problems using AI and ML frameworks.

Who Are We?

CompAsia is a digital and technology company that provides end-to-end solutions for certified pre-owned devices focusing on the mobile phone life-cycle value chain. Our focus is on delivering a mobile phone lifecycle experience not limited to Trading, Financing and Insurance.

What Role Will You Play in Shaping CompAsia's Future?

• Design, develop and enhance our object/defect detection models with primary focus on accuracy, precision, and recall.
• Collaborate closely with cross-functional teams, including stakeholders, software engineers, and domain experts to understand project requirements and business objectives.
• Heavily involve in R&D work and fast prototyping of new models.
• Conduct in-depth analysis of model performance, identify areas for improvement, and implement strategies to enhance the overall accuracy of the object detection system.
• Utilize advanced machine learning techniques, such as hyperparameter tuning, transfer learning, and ensemble methods, to optimize and fine-tune existing models.
• Stay abreast of the latest advancements in object detection algorithms and contribute to the adoption of state-of-the-art methodologies to improve model capabilities.
• Design and implement scalable and efficient machine learning pipelines for data preprocessing, model training, and deployment.
• Work on the deployment strategies of the enhanced object detection model, ensuring seamless integration into production environments.
• Collaborate with stakeholders to communicate complex technical concepts and findings in a clear and understandable manner.
• Understand company challenges and how integrating AI capabilities can help lead to solutions.

What Qualifications and Experience Will You Bring to Excel in This Role?

• Bachelor's/Master's/Ph.D. degree in Computer Science, Machine Learning, Data Science, or a related field.
• Proven experience in developing and enhancing object detection models / defect detection models, with a focus on achieving higher accuracy.
• Strong programming skills in languages such as Python and proficient in machine learning frameworks (e.g., TensorFlow, PyTorch, Keras - CNN).
• Exposure to GenAI development will be advantageous.
• Solid understanding of computer vision concepts, image processing, and feature extraction techniques.
• Experience with data preprocessing, annotation, and augmentation for object detection tasks.
• Excellent problem-solving skills and the ability to work independently and collaboratively in a dynamic team environment.
• Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

We are looking for passionate software engineers to join our ML Infrastructure team. Our team is at the forefront of building and maintaining scalable, high-performance infrastructure that empowers our machine learning engineers and data scientists to rapidly experiment with and innovate deep learning models for autonomous driving applications.

In this role, you'll initially focus on re-architecting and enhancing our dynamic provisioning platform for remote ML experiments, significantly improving the agility and efficiency of our ML development lifecycle. As we evolve, you'll contribute to broader ML infrastructure enhancements, ensuring our training ecosystem remains cutting-edge and robust.

What You’ll Be Doing:

• Collaborate closely with ML engineers and data scientists to understand their needs and develop robust tools and processes that streamline the entire ML development lifecycle.
• Participate in the design, development and optimization of our core ML infrastructure, leveraging Kubernetes to build robust solutions.
• Drive best practices in software engineering, including code quality, testing, and system reliability, within the ML platform.

What We’re Looking For:

• BS or MS in Computer Science or related field
• Strong knowledge of software engineering principles.
• Expertise with Python or Go
• Experience with AWS services or other Cloud platforms
• Strong written and oral communication skills
• Experience with the various stages of the ML development lifecycle is a plus
• Experience with ML frameworks such as PyTorch, Ray is a plus
• Experience with Kubernetes is a strong plus

We are looking for passionate software engineers to join our ML Infrastructure team. Our team is at the forefront of building and maintaining scalable, high-performance infrastructure that empowers our machine learning engineers and data scientists to rapidly experiment with and innovate deep learning models for autonomous driving applications.

In this role, you'll initially focus on re-architecting and enhancing our dynamic provisioning platform for remote ML experiments, significantly improving the agility and efficiency of our ML development lifecycle. As we evolve, you'll contribute to broader ML infrastructure enhancements, ensuring our training ecosystem remains cutting-edge and robust.

What You’ll Be Doing :

• Lead collaboration with ML engineers and data scientists to understand their complex needs and design and implement robust tools and processes that streamline the entire ML development lifecycle.
• Drive the design, development and optimization of our core ML infrastructure, leveraging Kubernetes to build robust solutions.
• Champion best practices in software engineering, including code quality, testing, and system reliability, within the ML platform.
• Take ownership of key infrastructure components and initiatives.
• Mentor junior engineers with high-level system design and code reviews

What We’re Looking For:

• 5+ years of professional experience in software engineering
• Strong knowledge of software engineering principles, distributed systems.
• Expertise with Python or Go
• Experience with AWS services or other Cloud platforms
• Experience with Kubernetes
• Strong written and oral communication skills
• Demonstrated ability to mentor and guide junior engineers
• Experience with the various stages of the ML development lifecycle is a plus
• Experience with ML frameworks such as PyTorch, Ray is a plus

Our team builds the foundational infrastructure that empowers Machine Learning Engineers to develop the next generation of self-driving technology. We design and operate the high-performance, large-scale systems that process petabytes of vehicle data, run massive simulations, and train complex models. This is a software engineering role focused on building robust platforms, not an SRE position.

As a Senior Engineer, you will be a key contributor to the team, owning the design and implementation of critical services that directly impact the pace of AV innovation.

What you'll be doing:

• Design, build, and deploy core components of our ML infrastructure platform on Kubernetes.
• Develop robust and scalable services that support the entire ML lifecycle, from data ingestion and processing to model training and evaluation.
• Write high-quality, maintainable code for high-throughput systems that handle petabytes of data.
• Own features and systems end-to-end, driving them from initial design through to production deployment and operation.

What we're looking for:

• 4+ years of professional software engineering experience.
• BS or MS in Computer Science or a related technical field.
• Hands-on experience developing and deploying applications on Kubernetes (k8s) is a must.
• Experience building or working on high-scale infrastructure or distributed backend systems.
• Strong proficiency in Python, Go, or a similar language.
• Solid experience with a major cloud provider (AWS, GCP, Azure).
• A strong sense of ownership and a passion for building high-quality software.