Mid-Level Machine Learning Engineer (Python)

Algorithm Development:

• Design, implement, and optimize advanced machine learning algorithms for ML development based on PyTorch or TensorFlow.
• Collaborate with cross-functional teams to understand business requirements and translate them into scalable and efficient ML models.
  

Model Training and Evaluation:

• Train, fine-tune, and evaluate machine learning models using large datasets.
• Implement and adhere to best practices for model validation, testing, and deployment.
  

Feature Engineering:

• Conduct comprehensive labeling, and feature engineering to enhance the performance of ML models.
• Collaborate closely with data scientists and domain experts to identify relevant features for improving model accuracy.
  

Python Development:

• Utilize Python programming language, focusing on PyTorch or TensorFlow, to develop machine learning solutions.
• Develop and maintain efficient and scalable code for production deployment in the VertexAI or similar.
  

Tooling and Infrastructure:

• Work with GCP cloud platforms to deploy and manage machine learning models.
• Implement and manage MLOps practices, including ML pipelines and CI/CD, for automated model deployment and monitoring.
  

Vector Database:

• Leverage vector databases to efficiently store and retrieve embeddings generated by ML models.
• Optimize data storage and retrieval processes for large-scale ML model applications.
  

Collaboration and Communication:

• Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to understand project requirements and deliver high-quality ML solutions.
• Communicate complex technical concepts related to ML model development to both technical and non-technical stakeholders.

Research and Innovation:

• Stay up-to-date with the latest advancements in ML models, and related fields.
• Contribute to the development and implementation of new techniques and technologies to improve language model performance.
  

Documentation:

• Maintain thorough documentation of code, algorithms, and methodologies specific to ML model development.
• Provide documentation and training to other team members as needed.
  

Qualifications:

• Bachelor's Degree or Master's in Computer Science, Machine Learning, or a related field.
• At least 4 years of experience in developing and deploying ML models.
• Strong proficiency in Python, especially with PyTorch, and familiarity with relevant libraries and frameworks.
• Experience with MLOps practices.
• Proven track record of successfully delivering ML model solutions in a production environment.
• Excellent problem-solving and critical-thinking skills.
• Strong communication and collaboration skills.

Working Visa:

• candidate must be a permanent resident or Citizen of Taiwan. We do not open this position for candidates required to apply for a Working Visa in Taiwan.

Stage 1 - Initial Call

• Conduct an initial phone call: This initial conversation assesses the candidate’s communication skills and motivation
  

Stage 2 - Technical Assessment

• Conduct a take-home assignment, and evaluate candidates' technical skills by relevant coding tests. The test will be based on an open-source project, and candidates have to create unit tests to demonstrate their ability to write code using best practices such as null safety, error handling, documentation, and coding standards.

Stage 3 - Technical Interviews

• Fundamental Skill: based on the take-home assignment, conduct code review sessions focusing on principles, system design, and areas relevant to the company’s tech stack. Include problem-solving sessions and coding exercises.

• Leadership and Design Skills: For senior positions, include discussions around system design, architecture, and leadership experiences. Assess their ability to mentor others, lead projects, and make high-level design decisions.

Stage 4 - Behavioral Interview

• Soft Skills: problem-solving abilities, and how the candidate has handled past challenges on teamwork, conflict, and project management.

• Team Fit and Culture Fit: Include interviews or informal chats with potential team members to assess cultural and team fit. This helps ensure the candidate can work well within the team and aligns with the company’s values.

Stage 5 - Reference Checks

• Conduct Reference Checks: Speak with former managers to verify the candidate’s experiences, strengths, and areas for improvement.