Machine Learning Engineer Intern

Vectech Limited is looking for a Machine Learning Engineer Intern to help build and improve ML-powered product features end-to-end—from data preparation and model training to deployment and monitoring. You'll work with engineers and product teams to ship measurable improvements in performance, latency, or user experience. **What you'll do:** • Build ML prototypes and turn them into production-ready components (APIs, services, pipelines) • Prepare and clean datasets; design evaluation sets and metrics • Train, fine-tune, and optimize models (e.g., classification, ranking, NLP, speech, or vision—based on team needs) • Integrate models into applications (backend services, batch pipelines, or real-time inference) • Improve reliability and performance: reduce latency, speed up inference, and profile bottlenecks • Run experiments (A/B tests or offline evaluation) and communicate results clearly • Document work and contribute to engineering best practices (testing, reproducibility, code review) **Projects you might work on (examples):** • Build a data pipeline + baseline model for a new feature • Improve model quality through better labeling, augmentation, or hard-negative mining • Reduce inference latency and cost for an existing service • Create dashboards/monitoring for model performance drift **What you'll gain:** • Ownership of a real ML deliverable shipped to users (or production-like environment) • Mentorship from ML and software engineers • Exposure to production ML workflows: experiments → deployment → monitoring **How to apply:** • Apply on Nicehire.ai • Send Resume/CV to recruitment@vectech.co • GitHub/portfolio (optional but recommended) • Short note: 3–5 lines on what you've built and what you want to learn

• Currently pursuing a BS/MS/PhD in CS, EE, Math, Stats, or related field (or equivalent experience)
• Strong programming skills in Python; comfortable with data structures and debugging
• Familiarity with at least one ML framework: PyTorch or TensorFlow
• Solid understanding of ML fundamentals (training/validation, overfitting, metrics, error analysis)
• Ability to communicate clearly and collaborate in a team environment
• Nice-to-have: Experience with NLP/LLMs, computer vision, speech/audio, recommendation systems, or time-series
• Nice-to-have: Experience deploying models (Docker, REST/gRPC, FastAPI), or working with cloud (AWS/GCP/Azure)
• Nice-to-have: Data tooling (SQL, Spark, Airflow, DVC, MLflow, Weights & Biases)