Machine Learning Engineer

About the Role

Join our AI research and applied data science team. As a Machine Learning Engineer, you will design, train, and deploy predictive models and Generative AI systems that power Ambryxel's next generation of digital ventures. You will bridge the gap between complex research and production-grade software.

Responsibilities

• Design, build, and deploy scalable machine learning models in production environments.
• Architect and maintain data pipelines for model training and real-time inference.
• Collaborate with software engineers to integrate AI capabilities into our SaaS products.
• Research and implement Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures.
• Monitor and optimize model performance, accuracy, and inference latency.
• Participate in technical architecture discussions and code reviews.

Requirements

• Experience developing and deploying machine learning models in production environments.
• Deep expertise in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.
• Strong background in mathematics, statistics, and deep learning architectures.
• Experience with vector databases (Pinecone, Weaviate, etc.) and LLM integration.
• Understanding of software engineering best practices, including version control (Git) and testing.
• Ability to translate complex business problems into scalable machine learning solutions.

Preferred Qualifications

• Master's or Ph.D. in Computer Science, Artificial Intelligence, or a related field.
• Familiarity with ML Ops tools and cloud infrastructure (AWS SageMaker, GCP Vertex AI).
• Experience with building or fine-tuning transformer models.
• Contributions to open-source ML projects or published research papers.

What We Offer

• Opportunity to work at the cutting edge of applied Generative AI.
• Compute resources and budget for advanced model training and experimentation.
• Mentorship and collaboration with top-tier technical talent.
• A fast-paced, highly autonomous venture studio environment.
• Career growth and leadership opportunities as the AI team scales.