Applied ML Engineer: Production AI & Personalization

Bjak; United States; Remote; USD 120,000 - 210,000; Full time

Job summary

Aimlroles is building AI-native applications and looking for engineers who can take ownership of ML systems from model development to production. You will work on classification, retrieval, ranking, personalization, and evaluation to ship reliable ML-powered features.

We value practical ML problem-solving, strong coding discipline and the ability to balance quality, latency and cost while collaborating with product and engineering teams.

Qualifications

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience building and deploying ML models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Comfortable writing production-quality code and working with real-world datasets.
• Able to balance model quality, latency, cost and reliability.
• Strong ownership, problem-solving skills.

Responsibilities

• Build and improve ML models that power our AI-native applications.
• Develop efficient classifiers and optimize model accuracy, latency and cost.
• Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
• Build and maintain ML training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements based on real-world feedback.
• Collaborate effectively with engineering and product teams to deliver reliable ML-powered features.

Skills

ML foundations

Neural architectures

Production code

Ownership

Problem-solving

Tools

Python

PyTorch / JAX

Embeddings / vector search

Ranking models

Job description

Aimlroles is building AI-native applications and looking for engineers who can take ownership of ML systems from model development to production. You will work on classification, retrieval, ranking, personalization, and evaluation to ship reliable ML-powered features.

We value practical ML problem-solving, strong coding discipline and the ability to balance quality, latency and cost while collaborating with product and engineering teams.