Lead the algorithm selection, design, and prototyping of machine learning models to solve complex business problems, including recommendation, personalization, and predictive analytics.
Apply your expertise in statistical modeling and machine learning to perform deep data analysis, guide crucial feature selection, and identify opportunities for product improvement.
Own the full ML lifecycle, from breaking down discrete steps of a pipeline (e.g., with a DAG) to analyzing model implementations and improving their robustness in the wild.
Implement and manage robust model observability, tuning, and optimization processes to ensure sustained performance and accuracy post-deployment.
Develop and maintain data pipelines to process and prepare data for model training and evaluation.
Design and conduct A/B tests to evaluate model performance and its impact on key business metrics.
Collaborate closely with product managers and engineers to define problems and deliver effective AI-driven solutions.
Mentor other team members, champion best practices in machine learning engineering, and stay current with the latest advancements in the field.

Hands-on experience designing and deploying production-grade machine learning systems.
Strong foundational knowledge of various machine learning algorithms and a proven ability to select the appropriate methodology, avoiding a one-size-fits-all approach.
Proven experience in areas such as recommendation systems, personalization, natural language processing (NLP), or semantic search.
Expert-level programming skills in Python, with deep, hands-on experience using data science and ML libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch.
Experience with data storage technologies (e.g., SQL, NoSQL, Key-value) and their scaling characteristics.
Experience with large-scale data processing technologies (e.g., Spark, Beam, Flink) and associated patterns (Batch vs. Stream), with a deep understanding of when to use them.
Experience using cloud platforms (e.g., GCP) at scale.
Experience deploying ML-based solutions at scale using cloud-native services.
Excellent communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional team environment.

Competitive base salary.
De Minimis Allowance.
13th Month Pay: Equivalent to one month’s base salary, paid every December.
Medical Insurance: HMO coverage for the employee plus up to two dependents (employer-paid).
Sick Leave: 15 paid days per year, pro-rated from the start date.
Annual Leave: 28 days per calendar year (January 1 – December 31), pro-rated based on start date.
Birthday Leave: An extra paid day off for your birthday.
Learning Leaves: Ten paid learning days per year.
Philippine Public Holidays: Statutory holidays are included.
Bereavement Leave: 3 paid days.
Parental Leave: Maternity or Paternity leave.
Flexible Work Arrangement: Hybrid model (two to three days in the office).
Peer Recognition: 100 Bonusly points per month for peer recognition.
Diversity and Inclusion: Commitment to championing diversity and accessibility in the workplace.

Senior Machine Learning Engineer

Key skills