Senior Machine Learning Engineer (LTV & Signal Systems)
Job Overview
Location
Remote
Salary
USD 3,800 - 4,500 monthly
Employment Type
Contract
Work Arrangement
Remote
Sector
Information Technology & Software
Experience Level
Senior (5-8 years)
Application Deadline
March 6, 2026
About the Company
Job Description
GoFasti is seeking an experienced Senior Machine Learning Engineer to join a dynamic team, focusing on LTV and Signal Systems. This remote role is ideal for professionals based in Latin America who are passionate about building cutting-edge predictive models.
In this position, you will be responsible for the entire lifecycle of LTV prediction systems, from data ingestion and feature engineering to model deployment and monitoring. You will develop machine learning approaches tailored to the unique constraints of the adtech industry, such as delayed conversions and noisy attribution.
Key responsibilities include owning MLOps practices, ensuring reproducible training pipelines, CI/CD for models, and robust logging and alerting. You will collaborate closely with Product and Go-To-Market teams to align model objectives with business goals like profitability and repeat customer rates. Furthermore, you will contribute to defining and evolving the company’s Signal Engineering playbook.
We are looking for candidates with strong foundations in machine learning, proven hands-on experience building production ML systems, and comfort with modern data stacks and cloud environments. Essential skills include Python, SQL, Docker, and experience with BigQuery, GCP services, and various ML libraries.
To apply for this role, click the Apply button on this page and follow the instructions.
Required Skills
Key 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, and changing platform policies.
- Own MLOps, including reproducible training pipelines, CI/CD for models, logging, monitoring, and alerting.
- Implement 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.
Qualifications
- 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 skills: Python, SQL, Docker (2–4 years experience).
- Data & Warehouse experience: BigQuery, dbt-style transformations, event and transactional pipelines (Shopify, CRM, GA4, CDPs).
- Cloud experience: GCP (Cloud Run, Pub/Sub / queues, scheduled jobs), secure APIs and services.
- Machine Learning experience: scikit-learn, XGBoost / LightGBM / CatBoost, optional PyTorch, and MLflow or Weights & Biases for tracking.
- Orchestration experience: Airflow, Prefect, Dagster (approach matters more than the tool).
Benefits & Perks
- Competitive salary range of USD $3,800 - $4,500 per month, depending on seniority and skillset.
- Opportunity to work remotely with a global client.
- Be part of a dynamic and innovative Talent-as-a-Service platform.
How to Apply
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Posted Date
February 4, 2026
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