Role Overview
We are looking for an AI Engineer to drive the development of intelligent systems in a unique intersection of biology and machine learning. You will work on transforming experimental models into functional prototypes, focusing on computer vision and image analysis for real-world aquaculture applications.
Key Responsibilities
- Design and refine machine learning and classical algorithms, progressing them from early-stage concepts to deployable prototypes.
- Structure and execute experiments that inform core business and scientific challenges.
- Build and maintain pipelines for data processing, model training, and statistical validation.
- Enhance and expand existing codebases in deep learning and machine learning frameworks.
- Ensure rigorous documentation of proof-of-concept projects and experimental outcomes.
- Lead planning and execution of long-term research initiatives.
- Support talent acquisition by identifying and evaluating candidates in the AI domain.
- Keep current with advancements in AI, particularly in computer vision and scalable model deployment.
Required Qualifications
- Demonstrated experience in building computer vision and image processing systems.
- Minimum of three years in a machine learning engineering or research-focused role.
- Proficiency with Python-based AI/ML libraries such as PyTorch, scikit-learn, SciPy, and Matplotlib.
- Solid grasp of software design, data modeling, and architectural patterns.
- Ability to produce clean, maintainable, and high-performance code.
- Strong written and verbal communication skills in English.
- Advanced degree (MSc or PhD) in Computer Science, Engineering, Mathematics, or a related discipline. Exceptional candidates with a B.Sc. and substantial industry experience will also be considered.
Preferred Skills
- Publications in leading AI conferences or journals.
- Experience deploying AI models in production environments.
- Familiarity with containerization tools like Docker.
- Working knowledge of SQL and NoSQL databases such as MongoDB, and basic networking protocols.
- Hands-on experience with cloud platforms including GCP, AWS, or Azure.
- Proficiency in MLOps tools such as DVC, MLflow, Metaflow, or Databricks.
- Experience with CI/CD systems like Jenkins, GitLab CI/CD, Screwdriver, or Spinnaker.
Technology Environment
The engineering stack includes PyTorch, scikit-learn, SciPy, Matplotlib, Docker, SQL, MongoDB, Google Cloud Platform, AWS, Azure, DVC, MLflow, Metaflow, Databricks, Jenkins, GitLab CI/CD, Screwdriver, and Spinnaker.
Our Culture
We value commitment, creativity, and the development of technology that creates tangible benefits for society. Our work bridges disciplines, combining AI with biological systems to solve meaningful challenges in sustainable aquaculture.
