What are the responsibilities and job description for the Machine Learning Engineer position at Pelham Berkeley Search?
Job Summary The Data Management Engineering team is embarking on a transformative journey to define, enable, and drive our data strategy. We aim to engage with partners across technology, data, business, and operations to develop and deliver business outcomes.
Our team is seeking a motivated and detail-oriented Entry-Level Machine Learning Engineer to join our growing team. As a key contributor, you will help build, test, and deploy machine learning models that solve real-world problems and enhance our data-driven decision-making. This is a great opportunity for someone eager to apply their academic knowledge in a practical, fast-paced environment.
Major Responsibilities
Our team is seeking a motivated and detail-oriented Entry-Level Machine Learning Engineer to join our growing team. As a key contributor, you will help build, test, and deploy machine learning models that solve real-world problems and enhance our data-driven decision-making. This is a great opportunity for someone eager to apply their academic knowledge in a practical, fast-paced environment.
Major Responsibilities
- Assist in designing, training, and validating machine learning models using structured and unstructured data
- Collaborate with data scientists, analysts, and software engineers to develop production-ready ML solutions
- Preprocess and clean large datasets to prepare for modeling
- Conduct experiments, evaluate model performance, and tune hyperparameters
- Write reusable, well-documented code in Python (or similar languages)
- Contribute to model deployment pipelines and MLOps infrastructure
- Stay current with the latest research, tools, and trends in machine learning and AI
- Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or a related field
- 1 to 2 years of foundational knowledge of machine learning concepts and algorithms (e.g., regression, classification, clustering)
- Proficiency in Python and ML libraries such as Scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch
- Familiarity with data manipulation, feature engineering, and exploratory data analysis
- Understanding of model evaluation metrics and techniques
- Good communication and teamwork skills
- Internship or project experience involving machine learning or data science
- Familiarity with version control (e.g., Git), Jupyter notebooks, and cloud platforms (e.g., AWS, GCP, or Azure)
- Exposure to LLM and Agentic AI
- Exposure to SQL and basic database concepts
- Enthusiastic about learning and growing in the field of AI/ML