Company
: Confidential
Location
: Austin, TX (On‑site or Hybrid)
Employment Type
: Full-Time
Job Summary:
A technology organization in Austin is seeking an Entry-Level AI/ML Engineer with a U.S.-completed Master’s degree. This role is designed for strong technical graduates who want to work on real-world machine learning applications, data engineering pipelines, and emerging AI solutions. This position offers direct exposure to production-oriented ML systems, infrastructure, and model experimentation.
**Key Responsibilities
Machine Learning & Modeling**
- Assist in training, evaluating, and tuning supervised and unsupervised ML models.
- Develop ML experiments using Python, Scikit-learn, PyTorch, TensorFlow, or similar frameworks.
- Conduct hyperparameter optimization using grid search, random search, or early stopping methods.
- Implement baseline models and compare against advanced architectures.
- Participate in building feature engineering pipelines and transformation logic.
Data Engineering & Analysis
- Clean, preprocess, and validate structured and unstructured datasets.
- Perform exploratory data analysis to identify patterns, correlations, and anomalies.
- Implement data augmentation strategies for small datasets.
- Support labeling workflows and metadata management.
- Write Python scripts to automate data extraction and transformation tasks.
LLM & NLP Support
- Assist in testing LLM-based workflows for internal tools or automation.
- Implement prompt templates and evaluate output quality.
- Conduct model benchmarking and performance comparisons.
- Assist in creating evaluation datasets for NLP tasks.
Pipeline Development & Integration
- Contribute to building end-to-end ML workflows and reproducible pipelines.
- Implement logging, experiment tracking, and version control practices.
- Assist with deploying models to testing or staging environments.
- Work with engineers to debug and optimize inference performance.
- Support integration of models with APIs or internal platforms.
Technical Documentation & Reporting
- Document model assumptions, methodologies, and parameter configurations.
- Create clear experiment logs and structured reports.
- Assist in preparing internal presentations and status updates.
Collaboration & Engineering Suppor
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- Participate in code reviews and follow best practices for software quality.
- Work with multidisciplinary engineering teams on workflow integration.
- Support troubleshooting in data pipelines and model execution issues.
Required Qualifications
- Master’s degree completed in the United States (Computer Science, Data Science, ML/AI, Engineering, or related fields).
- Proficiency in Python and core ML libraries (NumPy, Pandas, Scikit-learn).
- Academic or internship experience with model training and evaluation.
- Understanding of ML concepts: bias/variance, evaluation metrics, validation techniques.
- Familiarity with deep learning frameworks (PyTorch or TensorFlow).
- Ability to analyze datasets and communicate insights clearly.
- Strong problem-solving and ability to break down technical tasks.
Preferred Skills
- Experience with LLM APIs or NLP techniques.
- Exposure to cloud ML tools (AWS Sagemaker, GCP Vertex, or Azure ML).
- Understanding of Git-based workflows.
- Experience with SQL or NoSQL databases.
- Exposure to MLOps concepts: Docker, pipelines, model monitoring.
Eligibility
- Must have completed a Master’s degree in the U.S.
- Must be legally authorized to work in the United States.