Lead Machine Learning Engineer

Job Description

Company Description

Horizon AI is a rapidly growing startup that leverages cutting-edge artificial intelligence and machine learning technologies to drive innovation and create meaningful impact. Our team is dedicated to solving complex problems and delivering transformative AI solutions. We are committed to fostering a collaborative and dynamic environment that encourages the development of groundbreaking ideas and technologies. Join us to be a part of an exciting journey in the world of AI and ML.

Role Description

This is a full-time remote role for a Lead Machine Learning Engineer. The Lead Machine Learning Engineer will be responsible for designing, developing, and implementing machine learning models and algorithms. The role involves leading a team of engineers, performing data analysis, and collaborating with cross-functional teams to deliver high-quality AI solutions. Daily tasks include researching new ML techniques, optimizing existing models, and ensuring the scalability and efficiency of machine learning systems.

Requirements

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 6+ years of industry experience in AI/ML engineering, with at least 2 years in a leadership role.
• Deep expertise in developing and fine-tuning generative AI models (e.g., LLMs, diffusion models, VAEs).
• Strong proficiency in deep learning frameworks like PyTorch or TensorFlow.
• Demonstrated experience with transformer-based architectures (e.g., GPT, BERT, LLaMA).
• Experience with model training using large-scale datasets, including data curation and preprocessing.
• Familiarity with reinforcement learning from human feedback (RLHF) and prompt engineering.
• Proven track record deploying generative models into production environments.
• Proficiency in Python and common ML libraries (NumPy, Pandas, Hugging Face Transformers, etc.).
• Experience building scalable ML pipelines using tools like MLflow, Airflow, or Kubeflow.
• Solid understanding of MLOps practices including CI/CD, model monitoring, and versioning.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and GPU/TPU optimization.
• Experience leading end-to-end AI projects and mentoring technical teams.
• Ability to evaluate trade-offs between model performance, latency, and resource cost.
• Knowledge of responsible AI practices including fairness, safety, and interpretability in generative models.
• Familiarity with vector databases (e.g., Pinecone, FAISS) and RAG (Retrieval-Augmented Generation) techniques.
• Strong problem-solving skills and ability to translate business needs into AI solutions.
• Excellent written and verbal communication skills for both technical and non-technical audiences.
• Up-to-date with the latest research in generative AI and capable of applying it practically.
• Comfortable working in fast-paced, research-to-production environments with agile teams.