Machine Learning Engineer

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April 25

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Stateside

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B2B • SaaS • HR Tech

Stateside is a company that specializes in sourcing, hiring, and managing top nearshore technical talent. With a focus on providing high-quality staffing solutions, they help businesses scale their tech teams efficiently and cost-effectively. Stateside is recognized for its quick time to hire and low attrition rates, offering services that include full-stack development, project management, and various engineering roles. They aim to facilitate smooth collaboration through cultural alignment and real-time integration of dedicated teams.

51 - 200 employees

Founded 2013

🤝 B2B

☁️ SaaS

👥 HR Tech

📋 Description

• We are looking for a highly skilled Machine Learning Engineer to join our AI and data science team. • In this role, you will design, develop, and deploy machine learning models and pipelines that power critical data-driven solutions across our organization. • You’ll collaborate with data scientists, software engineers, and product teams to deliver intelligent systems at scale. • Responsibilities • Design and implement machine learning models for classification, regression, recommendation, NLP, or time-series forecasting tasks. • Develop, test, and maintain scalable ML pipelines for training, validation, and inference. • Collaborate with data engineers to build efficient data ingestion and feature extraction systems. • Optimize model performance using techniques like hyperparameter tuning, cross-validation, and regularization. • Deploy models to production using MLOps practices with tools like MLflow, TFX, or SageMaker. • Monitor and maintain the health of deployed models, updating them as needed. • Document ML experiments, metrics, and decisions. • Work closely with cross-functional teams to identify machine learning opportunities and define technical solutions.

🎯 Requirements

• Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related field (Ph.D. a plus). • 3–5+ years of hands-on experience building machine learning models in production. • Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch. • Experience with ML pipeline tools (e.g., Airflow, Kubeflow, MLflow). • Familiarity with cloud services (AWS, GCP, or Azure) and model deployment. • Solid understanding of statistics, data structures, and algorithms. • Experience with version control (Git), containerization (Docker), and CI/CD for ML. • Preferred Qualifications • Experience with NLP or computer vision projects. • Familiarity with big data tools (e.g., Spark, Hadoop). • Experience using GPU-accelerated training environments.