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# Senior Machine Learning Engineer

- **Company:** Talener Group, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $180,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Bioinformatics, Cloud Computing, Data Cleansing, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Graph Neural Networks, Pytorch, Transfer Learning, Deep Learning, AI Platforms, Kubernetes, Machine Learning Operations, Docker
- **Published:** October 29, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pvbi3fvn74

## About the Role

* 5+ years of experience building and delivering machine learning solutions in production environments.
 * Strong Python development skills.
 * Deep experience with PyTorch, TensorFlow, JAX, or similar frameworks.
 * Strong understanding of model training, evaluation, optimization, and deployment.
 * Experience translating complex machine learning problems into measurable business outcomes.

Preferred

 * Experience in life sciences, chemistry, drug discovery, bioinformatics, or related scientific domains.
 * Knowledge of RDKit, Chemprop, federated learning, Kubernetes, Docker, and cloud-based ML infrastructure.

## Description

We are seeking a product-focused Machine Learning Engineer to help build and scale a next-generation AI platform used in drug discovery and scientific research. This individual will work across the full machine learning lifecycle, from data preparation and experimentation through model development, deployment, and optimization.

You will collaborate closely with software engineers, scientists, and product teams to transform advanced machine learning research into reliable, production-ready solutions that deliver real-world impact.

What You'll Do

 * Design, build, and deploy machine learning models that support scientific discovery and predictive analytics.
 * Develop solutions involving graph neural networks, transfer learning, molecular prediction models, and modern deep learning techniques.
 * Create scalable training, evaluation, and model selection pipelines.
 * Curate and improve large scientific datasets while ensuring data quality and reproducibility.
 * Partner with engineering teams to deploy containerized ML services in cloud-based environments.
 * Research and evaluate emerging machine learning approaches for incorporation into the platform.