About NeuroHire
NeuroHire is a Texas-based AI-powered SaaS company developing intelligent solutions for the modern hiring ecosystem. Our platform combines Artificial Intelligence, Machine Learning, automation, and job-market data to help users discover, organize, and act on career opportunities more efficiently.
We are expanding our AI engineering team and are looking for a Machine Learning Engineer to help develop, evaluate, and deploy machine learning systems that power NeuroHire’s products.
Role Overview
As a Machine Learning Engineer, you will work across the ML lifecycle—from data preparation and feature engineering to model development, evaluation, deployment, and monitoring.
You will collaborate with Data Scientists, AI Engineers, Software Engineers, Data Engineers, and Product teams to turn machine learning research and experiments into reliable production capabilities.
This is an entry-level opportunity for candidates with hands-on experience through academic projects, internships, research, Kaggle competitions, GitHub projects, or personal machine learning projects.
What You’ll Do
- Develop, train, evaluate, and improve machine learning models.
- Prepare and preprocess structured and unstructured datasets.
- Perform feature engineering and feature selection.
- Implement supervised and unsupervised learning techniques.
- Build models for classification, regression, ranking, recommendation, and prediction.
- Work with NLP and text-based datasets.
- Develop machine learning solutions for job matching, recommendations, search, ranking, and personalization.
- Experiment with different algorithms, architectures, and hyperparameters.
- Evaluate models using appropriate performance metrics.
- Perform error analysis and investigate model failures.
- Collaborate with Data Scientists on experimentation and model development.
- Work with Software Engineers to integrate ML models into production applications.
- Develop and maintain machine learning pipelines.
- Support model deployment through APIs and cloud infrastructure.
- Monitor model performance, data quality, and model drift.
- Document experiments, model architectures, datasets, and results.
- Participate in code reviews and follow software engineering best practices.
- Research emerging developments in Machine Learning, Generative AI, and AI engineering.
What We’re Looking For
- Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Statistics, Mathematics, Computer Engineering, or a related field.
- 0–1 year of experience in Machine Learning, AI, Data Science, or Software Engineering.
- Strong Python programming skills.
- Strong understanding of machine learning fundamentals.
- Knowledge of statistics, probability, and linear algebra.
- Experience with machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch.
- Familiarity with data preprocessing and exploratory data analysis.
- Understanding of model evaluation and validation techniques.
- Familiarity with SQL and working with datasets.
- Strong analytical and problem-solving skills.
- Ability to communicate technical findings clearly.
Academic projects, internships, research, Kaggle competitions, hackathons, and personal ML projects can be considered relevant experience.
Nice to Have
- Experience with PyTorch or TensorFlow.
- Knowledge of NLP and transformer-based models.
- Experience with Large Language Models (LLMs).
- Familiarity with Generative AI and RAG.
- Knowledge of recommendation systems and ranking algorithms.
- Experience with XGBoost, LightGBM, or other gradient-boosting frameworks.
- Familiarity with Hugging Face Transformers.
- Experience with MLflow or similar experiment-tracking platforms.
- Knowledge of Docker and Kubernetes.
- Experience with AWS, Azure, or Google Cloud.
- Familiarity with CI/CD and MLOps practices.
- Knowledge of vector databases and embeddings.
- Experience with model serving and inference APIs.
- Contributions to GitHub, open-source projects, research, or Kaggle.