Machine Learning Engineer

2 weeks ago

Singapore GPT DESK PTE. LTD. Full time

**Responsibilities**:

- **Literature Review and Research**:Conduct an in-depth review of academic publications, whitepapers, and industry trends to align technical solutions with business needs.
- **Algorithm Development**:Design, implement, and fine-tune machine learning algorithms, utilizing large language models (LLMs), deep neural networks, and traditional machine learning techniques.
- **Adaptation to Business Requirements**:Continuously assess the performance of deployed solutions and adjust algorithms to better align with evolving business objectives.
- **Data Preprocessing**:Prepare and clean data sets to ensure high-quality inputs for training and validating models.
- **Performance Metrics**:Establish and monitor key performance indicators to evaluate the effectiveness of machine learning models.
- **Stakeholder Communication**:Collaborate closely with product managers, data scientists, and other stakeholders to integrate machine learning features seamlessly into larger projects.
- **Continual Learning**:Stay abreast of the latest technological advancements in the field of machine learning to continually bring innovative solutions to the business.

**Basic Requirements**:

- Passionate about work, willing to actively learn new knowledge in related areas.
- Good communication skills with colleagues, understanding business needs.
- Proficient in Python programming.
- Skilled in data manipulation with Numpy, Pandas, etc.
- Proficient in building neural networks using frameworks like Torch, TensorFlow, and understand their working mechanisms.
- Basic knowledge of traditional machine learning algorithms, including classifiers/regressors, clustering, etc.
- Experience in one of the following areas based on neural networks: Speech Processing, Natural Language Processing or Image Processing

**Bonus Points**:

- Experience in training or using large language models.
- Experience in distributed neural network training or deployment.
- Research experience in neural networks.
- Published papers in related fields.
- Experience in tuning reinforcement learning algorithms like PPO for better results.
- Experience in tuning models with over 7B parameters for improved results.

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