Machine Learning Engineer

• 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.

• 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.

Our client is seeking a Machine Learning Engineer to join their growing team and help build, deploy, and scale ML-powered products that directly impact customer outcomes. This is a highly collaborative, product-focused role where you’ll work alongside Product, Engineering, and Data teams to move machine learning solutions from concept to production and drive measurable business value.

This is not a research-only position. We’re looking for someone who has successfully delivered machine learning solutions end-to-end, from problem definition and experimentation through deployment, monitoring, and ongoing optimization. Remote with some travel to NJ.