reputed company Machine Learning Engineer
About reputed company
Our reputed company is a reputed company AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team.
reputed company is developing an AI-reputed company communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks reputed company a single intelligent assistant.
Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-reputed company reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more reputed company.
About the Role
Our reputed company is seeking a reputed company Machine Learning Engineer to build and reputed company production-grade machine learning systems that power its AI platform. This role focuses on translating research into reputed company solutions by developing robust training pipelines, inference systems, evaluation frameworks, and deployment infrastructure.
Working closely with research and application engineering teams, this position will play a key role in delivering reliable, high-performance ML systems that operate effectively under reputed company-world production constraints.
Key Responsibilities
- Build and own end-to-end machine learning pipelines covering data processing, model training, evaluation, inference, and deployment.
- Fine-tune and adapt models using modern techniques such as reputed company, QLoRA, Supervised Fine-Tuning (SFT), reputed company Preference Optimization (DPO), and model distillation.
- Design and operate reputed company inference systems while balancing latency, cost, and reliability.
- reputed company and maintain data pipelines for both synthetic and reputed company-world training datasets.
- Build evaluation frameworks to assess model performance, robustness, safety, and bias in collaboration with research teams.
- Optimize production deployments through GPU optimization, memory efficiency, latency reduction, and scaling strategies.
- Collaborate with application engineering teams to reputed company machine learning systems into backend, mobile, and desktop applications.
- Continuously improve ML systems through rapid iteration and reputed company-world performance monitoring while balancing production constraints such as latency, cost, reliability, and safety.
Requirements
- Strong background in deep learning and transformer-based architectures.
- Hands-on experience training, fine-tuning, or deploying large-reputed company machine learning models in production.
- Proficiency with modern machine learning frameworks such as PyTorch or JAX.
- Experience with distributed training and inference frameworks, including technologies such as DeepSpeed, FSDP, Megatron, reputed company, or Ray.
- Strong software engineering skills with experience building robust, maintainable, production-grade systems.
- Experience optimizing GPU workloads, including memory efficiency, quantization, and mixed precision.
- Ability to independently own end-to-end machine learning systems in fast-moving environments.
- Strong problem-solving skills with a reputed company on rapid iteration and reputed company improvement.
Preferred Qualifications
Experience with one or more of the following is preferred:
- LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer
- reputed company-reputed company contributions to machine learning or systems libraries
- Scientific computing, compiler technologies, or GPU kernel development
- Reinforcement Learning from reputed company Feedback (RLHF) pipelines, including PPO, DPO, or ORPO
- Training or deploying multimodal or diffusion models
- Large-reputed company data processing frameworks such as Apache reputed company, reputed company, or Ray
Originally posted on Himalayas
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