GoFasti · 3 hours ago
1003- Senior Machine Learning Engineer (LTV & Signal Systems)
GoFasti is a Talent-as-a-Service company that connects top talent from LatAm with leading companies globally. They are seeking a Senior Machine Learning Engineer to design and deploy LTV prediction systems, collaborate with various teams, and own MLOps processes.
Human ResourcesInformation TechnologySoftwareSoftware Engineering
Responsibilities
Design, build, and deploy end-to-end LTV prediction systems, covering:Data ingestion, Feature engineering, Model training and evaluation and Deployment and monitoring
Develop ML approaches that work within adtech constraints, such as:Delayed or sparse conversions, Noisy attribution, Changing platform policies
Own MLOps, including: Reproducible training pipelines, CI/CD for models, Logging, monitoring, and alerting
Data quality checks and drift detection
Collaborate closely with Product and GTM teams to translate business goals (profitability, payback, repeat rate) into model objectives
Help define and evolve the company’s Signal Engineering playbook: What signals are computed, How often they’re updated and How they’re delivered to downstream systems
Qualification
Required
Strong foundations in machine learning, with the ability to reason from: Business objective, data limitations, model choice and deployment
Hands-on experience building production ML systems (not just notebooks), including: Training pipelines, Deployment and Monitoring
Experience with LTV modeling, such as: Probabilistic or BTYD-style approaches, Survival or retention modeling, Regression/classification for value prediction and Model calibration
Comfortable working with modern data stacks and cloud environments
Core: Python, SQL, Docker (2–4 years experience)
Data & Warehouse: BigQuery, dbt-style transformations, event and transactional pipelines (Shopify, CRM, GA4, CDPs)
Cloud: GCP (Cloud Run, Pub/Sub / queues, scheduled jobs), secure APIs and services
Machine Learning: scikit-learn, XGBoost / LightGBM / CatBoost, optional PyTorch And MLflow or Weights & Biases for tracking
Orchestration: Airflow, Prefect, Dagster (approach matters more than the tool)
Preferred
Adtech/Martech experience: Meta CAPI, Google Ads/Enhanced Conversions/Offline Conversions, audience/CRM activation, conversion quality, incrementality intuition
Identity/data joining experience (hashed PII, multi-key matching, deduping, event stitching)
Experience with streaming/near-real-time systems and event pipelines
Familiarity with experimentation frameworks (uplift/incrementality), MMM/attribution constraints, or measurement-heavy environments