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 (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 (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 inference engines (ONNX Runtime, TensorRT, TFLite, etc.) to test model compatibility and performance.
- Optimize models for inference (quantization, pruning, batching).
Application Integration
- Build prototype applications (web or backend) that integrate ML 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 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 in CS, Data Science, EE, or related field.
- Strong programming skills in C++ & Python, with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Familiarity with web technologies: JavaScript, Node.js, HTML, CSS.
- Understanding of model deployment and inference (willingness to learn at least one framework: ONNX, TensorRT, TF-Lite).
- Good problem-solving skills, ability to run experiments and analyze results.
- Strong written communication skills for documentation and reporting.
Nice-to-haves:
- Experience with GPU programming or hardware-aware optimizations.
- Exposure to MLOps or model serving tools (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.
Apply
Send your application to hr@binaire.app including:
- CV
- Portfolio or relevant work
- LinkedIn profile
- Contact details
- Past working experience and any public work including research papers, patents, or publications with links and references