Machine Learning Engineer

Who are we looking for

We aren't looking for employees; we are looking for mission-driven pioneers. To thrive at Gogolook, you must meet the following standards:

• Impact-Driven Architects: We require individuals who are motivated by the challenge of creating meaningful products that defend society against global fraud.
• Relentless Learners: We expect you to take full ownership of your growth by actively participating in technical communities and workshops, representing Gogolook's expertise on the global stage.
• Assertive Experts: We value those who bring their own professional opinions to the table, engaging in rigorous discussion to ensure we build only the most exceptional products.
• Radical Collaborators: You must thrive in a culture of extreme transparency, where you are expected to consume company-wide information and actively participate in shaping our collective future.

If you feel you fit the bill, come join us!

About the Intelligent Systems Lab (ISL)

The ISL team provides applied research and development capabilities to help achieve long-term strategic goals for Gogolook. Our typical project lifecycle is 6 months, focusing on a transition from ISL team principles to long-running applied R&D. We collaborate closely with other Gogolook teams to:

• Evaluate product needs for intelligent solutions.
• Propose solutions that use cutting-edge AI.
• Create POCs (Proof of Concepts) to demonstrate viability.

Together, ISL and Engineering teams meet both the short and long-term technology needs of the company.

Role Mission

As a Machine Learning Engineer, you will be a core part of ISL's modeling competency. Your mission is to implement, evaluate, and deploy machine learning models that turn Gogolook's first-party data into precise, reliable products that protect users from fraud. You will treat model quality as an experimental discipline - measured, not assumed - and bring statistical rigor to every result you ship.

Your journey at Gogolook is designed for growth. At the beginning, you will build and train models with guidance, evaluate them with the right metrics, prepare and engineer the data they learn from, and document your work so others can reproduce it. Subsequently, we expect you to take full ownership of end-to-end ML solutions - designing the experiments, running the statistical analysis, optimizing for performance, and deploying to production. Over time you may lead advanced ML initiatives, guide experimental strategy, drive statistical rigor across the team, architect scalable ML infrastructure, and mentor junior engineers.

Responsibilities

• Model Development: Implement and train supervised and unsupervised models using standard frameworks under structured guidance.
• Model Evaluation: Evaluate models with appropriate metrics and perform error analysis to understand where and why a model fails.
• Data Preprocessing & Feature Engineering: Clean, transform, and engineer features to prepare data for training and testing.
• Experimentation: Apply basic statistical-analysis and experimental-design methods to tune and optimize models.
• Deployment Support: Help package, deploy, and monitor models within ISL's infrastructure, learning CI/CD and containerization practices.
• Collaboration: Work in a shared codebase using Git; partner with Data Research Engineers, AI Engineers, and Product Managers to deliver value.
• Documentation: Clearly document training processes, experiment design, assumptions, and outcomes, and communicate results to the team.

Qualifications

• Proficiency in English, both spoken and written, sufficient to collaborate, document, and present in an English-working environment.
• Bachelor's degree in Computer Science, Statistics, Machine Learning, or a related quantitative field.
• 2-4 years of experience in the related area depending on degree.
• Hands-on experience building, training, and evaluating machine learning models with standard frameworks.
• Highly independent and self-motivated, with the ability to manage multiple task items within a team environment.
• Ability to learn from mistakes and a continuous drive to seek self-improvement in new skills and technical knowledge.
• Strong collaborative mindset, acting as a good team player who understands how value is delivered both efficiently and responsively.
• Proactive communicator who takes the initiative to voice concerns regarding technical risks and contributes to the creation and reduction of tech debt.

Preferred Qualifications

• Exposure to cloud ML platforms (AWS SageMaker, Google Vertex AI) for training and deployment.
• Familiarity with CI/CD and containerization (Docker) for shipping models to production.
• Comfortable working with Data Research Engineers, AI Engineers, and Product Managers to bridge data, modeling, and product.

Skills & Competencies

• Solid grasp of ML fundamentals - supervised and unsupervised learning, and model evaluation metrics.
• Familiarity with popular frameworks: scikit-learn, XGBoost, and TensorFlow or PyTorch.
• Experience with data preprocessing and feature engineering.
• Basic statistics for model tuning and basic experimental-design methods for model optimization.
• Proficiency in Python; comfortable with Git and collaborative coding.
• Disciplined in clearly documenting training processes, underlying assumptions, and final results.