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# Machine Learning Engineer

- **Company:** Allied Llc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Microsoft Azure, BigQuery, Software as a Service, Information Engineering, Database Queries, Python (Programming Language), Machine Learning, Routing, Recommender Systems, Tensorflow, Supervised Learning, Feature Engineering, Pytorch, Large Language Models, Snowflake, Generative AI, Agentic-AI, Scikit Learn, Data Analytics, Xgboost, Data Management, Machine Learning Operations, Drift Detection, Software Version Control, Data Pipelines, Databricks
- **Published:** October 22, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pthluukiqo

## About the Role

* 5+ years of experience in Machine Learning Engineering, Applied Data Science, or a related field.
 * Proven experience building and deploying machine learning models into production environments.
 * Strong Python programming skills.
 * Hands-on experience with machine learning frameworks such as:
 * Scikit-learn
 * XGBoost
 * PyTorch
 * TensorFlow
 * Strong understanding of:
 * Supervised learning
 * Forecasting
 * Recommendation systems
 * Optimization
 * Statistical modeling
 * Experimentation frameworks
 * Experience working with large-scale, real-world datasets.
 * Strong SQL skills.
 * Experience with modern cloud data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
 * Experience deploying solutions in AWS, Azure, or GCP environments.
 * Familiarity with MLOps concepts including:
 * Feature engineering pipelines
 * Model versioning
 * Monitoring
 * Drift detection
 * Automated retraining workflows
 * Excellent communication and stakeholder management skills., * Experience developing ML-powered SaaS products.
 * Experience with forecasting, optimization, customer behavior modeling, route optimization, workforce management, pricing, scheduling, or operational intelligence solutions.
 * Experience with LLMs, Generative AI, and agentic workflows in production environments.
 * Experience designing and analyzing A/B tests.
 * Experience operating machine learning systems at scale.
 * Previous experience in a Senior, Lead, or Staff-level Machine Learning or Data Science role.

## Description

* Own the end-to-end development of machine learning solutions powering customer-facing products.
 * Translate business and customer challenges into ML opportunities, requirements, and measurable success metrics.
 * Design, build, deploy, and maintain predictive, forecasting, recommendation, ranking, optimization, and decision-intelligence models.
 * Partner with Product Managers and Software Engineers to integrate ML capabilities into applications and workflows.
 * Balance model performance, scalability, explainability, maintainability, and customer value.

Model Deployment & MLOps

 * Take models from discovery through experimentation, validation, deployment, monitoring, and continuous improvement.
 * Develop evaluation frameworks to measure model quality, adoption, business impact, and drift.
 * Design and execute A/B tests and experimentation strategies to validate ML-driven product enhancements.
 * Build feedback loops and retraining processes that improve model performance over time.
 * Collaborate with Data Engineering teams to ensure reliable data pipelines and production infrastructure.

Product & Technical Leadership

 * Identify opportunities where machine learning can create meaningful product differentiation.
 * Make practical trade-offs between complexity, speed, explainability, and business value.
 * Influence product roadmaps and technical direction through data-driven recommendations.
 * Evaluate and apply the right solution for the problem, whether traditional ML, statistical models, LLMs, GenAI, heuristics, or rule-based approaches.
 * Mentor team members and promote best practices across machine learning initiatives.

Collaboration & Communication

 * Work closely with Product, Engineering, Design, Data Engineering, and customer-facing teams.
 * Communicate complex technical concepts to both technical and non-technical stakeholders.
 * Build solutions that prioritize customer trust, usability, and measurable outcomes.
 * Stay closely connected to customer pain points to ensure ML solutions solve real-world business challenges.