Machine Learning Intern

About Us

At Binaire, we build multi-modal inference systems and frontier text and image models that power next-generation applications. As a fast-growing tech company, we combine cutting-edge research with product-driven engineering to deliver scalable, AI-powered solutions. Join us as a Machine Learning Intern and contribute to real-world ML systems — from model training to deployment.

Role & Responsibilities

As a Machine Learning Intern, you will:

• Build & Train Models

  • Design, implement, and train machine learning models (e.g., supervised, unsupervised, or deep learning).
  • Preprocess data, engineer features, and conduct experiments to improve model accuracy.

• Benchmark & Test

  • Define, run, and maintain benchmarking pipelines to compare model performance (e.g., latency, accuracy, memory).
  • Evaluate models on different datasets and quality metrics.
  • Create new benchmarks for key use-cases, to measure model behavior under diverse conditions.

• Deploy & Inference

  • Deploy models to production or test environments for inference.
  • Work with multiple inference engines (e.g., ONNX Runtime, TensorRT, TFLite, or similar) to test model compatibility and performance.
  • Optimize models for inference (quantization, pruning, batching).

• Application Integration

  • Build prototype applications (web or backend) that integrate machine learning models.
  • Use web technologies (JavaScript, Node.js, HTML, CSS) to create front-ends or APIs for serving predictions.

• Reporting & Documentation

  • Create detailed reports on model training results, benchmarking outcomes, and deployment performance.
  • Document your experiments, code, and architecture decisions for internal teams and stakeholders.

• Research & Innovation

  • Explore new techniques, architectures, or inference frameworks.
  • Propose and implement improvements to the model lifecycle, such as faster inference, better accuracy, or resource efficiency.

Qualifications & Skills

Must-haves:

• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related field.
• Strong programming skills in C++ & Python, with experience in ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Familiarity with web technologies: JavaScript, Node.js, HTML, CSS.
• Understanding of model deployment and inference: knowledge (or willingness to learn) of at least one inference engine/framework (e.g., ONNX, TensorRT, TF-Lite).
• Good problem-solving skills, and ability to run experiments and analyze results.
• Strong written communication skills — ability to write clear reports and document code.

Nice-to-haves:

• Experience with GPU programming or hardware-aware optimizations.
• Exposure to MLOps or model serving tools (e.g., Docker, Kubernetes, MLflow).
• Knowledge of benchmarking tools and metrics for ML (latency, throughput, memory).
• Prior experience building simple web apps or APIs.
• Familiarity with version control (Git) and collaborative workflows.

What You’ll Get

• Hands-on experience in the entire ML lifecycle: from data to deployment
• Mentorship by senior ML engineers and researchers
• Opportunity to contribute to real products and production-grade systems
• Exposure to inference engine optimization
• A chance to present your findings and build benchmarks that guide future work
• Potential for full-time conversion depending on performance