Lola Blankets is hiring a Senior GenAI / Machine Learning Engineer to join the team supporting our Places ecosystem. You will design, build, and deploy LLM‑powered and ML‑driven systems at production scale, working on advanced data pipelines and next‑generation AI capabilities for global location and mapping products.

What You'll Do

Build and optimize GenAI and LLM‑based solutions including prompt engineering, fine‑tuning, and model evaluation.
Develop applied machine learning models for large‑scale data processing, classification, enrichment, and automation.
Design and implement robust Python‑based pipelines using modern ML frameworks like PyTorch, TensorFlow, and HuggingFace.
Build scalable data engineering workflows, including ingestion, transformation, and feature pipelines.
Deploy and maintain production ML systems, ensuring reliability, observability, and performance.
Collaborate with cross‑functional teams to refine requirements, validate model outputs, and integrate ML components.
Apply best practices for model lifecycle management, including versioning, monitoring, retraining, and cost‑efficient deployment.
Contribute to engineering standards, code reviews, and continuous improvement initiatives.

What We're Looking For

Strong expertise in GenAI / LLM engineering with hands‑on experience in modern frameworks and tooling.
Proven experience in applied machine learning, including model development, evaluation, and optimization.
Advanced Python programming skills and deep familiarity with ML libraries and ecosystem tools.
Solid understanding of data engineering foundations, including ETL pipelines, distributed processing, and data quality.
Demonstrated experience deploying production ML systems (batch or real‑time).
Experience with cloud platforms (AWS preferred) for scalable ML and data workloads.
Strong understanding of software engineering best practices, version control, CI/CD, and testing.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field.
5+ years of experience in ML engineering, data engineering, or software engineering with a strong ML focus.
Ability to write clean, efficient, and maintainable code.
Strong analytical mindset, problem‑solving skills, and attention to detail.
Comfortable working in fast‑paced, agile environments.

Nice to Have

Experience working with geospatial or maps‑related datasets.
Knowledge of cost optimization strategies for ML workloads and cloud infrastructure.
Familiarity with the Places ecosystem or similar location‑based data domains.
Exposure to workflow orchestration tools like Airflow, Prefect, or Luigi.
Experience with containerization and orchestration using Docker or Kubernetes.