Applied Machine Learning (ML) Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Oct 20, 2026.
Applied Machine Learning (ML) Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home
The Applied Machine Learning (ML) Engineer will build and improve Machine Learning (ML) systems that power our AI-native applications. You will work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment. This role is for engineers who enjoy solving practical Machine Learning (ML) problems, improving model quality and shipping reliable systems used by real users. Machine Learning (ML) and Artificial Intelligence (AI) experience are required. This position is 100% Remote.
MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT.
Applied Machine Learning (ML) Engineer Responsibilities:
- Build and improve Machine Learning (ML) models for classification, ranking, retrieval and personalization.
- Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
- Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
- Build personalization models that learn from user behavior while respecting privacy.
- Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
- Fine-tune and adapt existing models when supported by sufficient data.
- Build and maintain Machine Learning (ML) training, inference and deployment pipelines.
- Debug production issues, monitor model performance and ship improvements based on real-world feedback.
Applied Machine Learning (ML) Engineer Outcomes:
- Machine Learning (ML) models in production meet expected accuracy, relevance, latency and reliability targets.
- Classification and ranking improvements deliver measurable gains in model quality.
- Evaluation pipelines identify regressions and support reliable model releases.
- Training, inference and data pipelines are robust, reproducible and maintainable.
- Model improvements are driven by real-world feedback and measurable results.
- Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.
Applied Machine Learning (ML) Engineer Qualifications:
- Machine Learning (ML) experience is required.
- Artificial Intelligence (AI) experience is required.
- Strong foundations in Machine Learning (ML) and modern neural architectures.
- Hands-on experience building and deploying Machine Learning (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
- Tech Stack: Embeddings, vector search and ranking models, Production Machine Learning (ML) systems and inference pipelines, Python, and PyTorch/JAX.
Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.
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