Skills

Must have

Proficiency in Python with solid software engineering practices
Experience with at least one major ML framework (e.g., PyTorch, TensorFlow)
Strong understanding of machine learning fundamentals
Training, evaluation, generalization, and performance trade-offs
Familiarity with core algorithms and mathematical concepts commonly used in applied ML systems
e.g., Kalman filtering, assignment / matching problems (Hungarian algorithm), optimization basics
Ability to move ML work beyond experimentation into reliable, usable systems
Comfortable working with changing requirements and problem domains

About the Role

We are seeking a Machine Learning Engineer with strong engineering fundamentals to build production-ready ML systems.
The problem space is not fixed and may evolve over time, including (but not limited to) computer vision, tracking, LLM/VLM, or other applied ML domains.
Rather than focusing on a single research direction, this role emphasizes practical execution, system integration, and long-term operability.
You will work closely with software, platform, and infrastructure teams to ensure ML components are efficient, reliable, and maintainable in real production environments.

Responsibilities

Model Development & Evaluation
Train, fine-tune, and evaluate machine learning models across different problem domains
Understand data characteristics, evaluation metrics, and real-world failure modes
Algorithm Understanding & Application
Apply and adapt core algorithms commonly used in applied ML systems
Understand and work with fundamental methods such as state estimation, data association, and optimization when relevant
Inference & Performance Optimization
Optimize inference latency, throughput, and resource utilization
Make informed trade-offs between model quality, performance, and system constraints
Productionization & Integration
Package models into reusable tools, libraries, or services
Integrate ML components into existing systems with clear configuration and runtime controls
Support deployment and validation in production (cloud and/or edge)
System Debugging & Collaboration
Diagnose issues across model behavior, runtime performance, and system stability
Collaborate with cross-functional teams and clearly communicate technical constraints

Required Qualifications

Proficiency in Python with solid software engineering practices
Experience with at least one major ML framework (e.g., PyTorch, TensorFlow)
Strong understanding of machine learning fundamentals
Training, evaluation, generalization, and performance trade-offs
Familiarity with core algorithms and mathematical concepts commonly used in applied ML systems
e.g., Kalman filtering, assignment / matching problems (Hungarian algorithm), optimization basics
Ability to move ML work beyond experimentation into reliable, usable systems
Comfortable working with changing requirements and problem domains

[台北] Machine Learning Engineer

Compensation

Not disclosed

Location

Daan District, Taipei City, Taiwan

Work setup

Not specified

Seniority

Not specified

Employment

Not specified

Experience

Not specified

Proficiency in Python with solid software engineering practices
Experience with at least one major ML framework (e.g., PyTorch, TensorFlow)
Strong understanding of machine learning fundamentals
Training, evaluation, generalization, and performance trade-offs
Familiarity with core algorithms and mathematical concepts commonly used in applied ML systems
e.g., Kalman filtering, assignment / matching problems (Hungarian algorithm), optimization basics
Ability to move ML work beyond experimentation into reliable, usable systems
Comfortable working with changing requirements and problem domains

About the Role

We are seeking a Machine Learning Engineer with strong engineering fundamentals to build production-ready ML systems.
The problem space is not fixed and may evolve over time, including (but not limited to) computer vision, tracking, LLM/VLM, or other applied ML domains.
Rather than focusing on a single research direction, this role emphasizes practical execution, system integration, and long-term operability.
You will work closely with software, platform, and infrastructure teams to ensure ML components are efficient, reliable, and maintainable in real production environments.

Responsibilities

Model Development & Evaluation
Train, fine-tune, and evaluate machine learning models across different problem domains
Understand data characteristics, evaluation metrics, and real-world failure modes
Algorithm Understanding & Application
Apply and adapt core algorithms commonly used in applied ML systems
Understand and work with fundamental methods such as state estimation, data association, and optimization when relevant
Inference & Performance Optimization
Optimize inference latency, throughput, and resource utilization
Make informed trade-offs between model quality, performance, and system constraints
Productionization & Integration
Package models into reusable tools, libraries, or services
Integrate ML components into existing systems with clear configuration and runtime controls
Support deployment and validation in production (cloud and/or edge)
System Debugging & Collaboration
Diagnose issues across model behavior, runtime performance, and system stability
Collaborate with cross-functional teams and clearly communicate technical constraints

Required Qualifications

Proficiency in Python with solid software engineering practices
Experience with at least one major ML framework (e.g., PyTorch, TensorFlow)
Strong understanding of machine learning fundamentals
Training, evaluation, generalization, and performance trade-offs
Familiarity with core algorithms and mathematical concepts commonly used in applied ML systems
e.g., Kalman filtering, assignment / matching problems (Hungarian algorithm), optimization basics
Ability to move ML work beyond experimentation into reliable, usable systems
Comfortable working with changing requirements and problem domains