Machine Learning Engineer, Human Native

• Hands on experience developing and deploying ML models and ML data pipelines in production
• Strong Statistical Analysis & Data Evaluation, you’re comfortable developing or learning to develop custom metrics, identify biases, and quantify data quality
• Strong Python skills for Data & Machine Learning, familiarity with PyTorch and TensorFlow
• Experience with distributed computing and big data — scaling ML pipelines for large datasets
• Familiarity with cloud-based deployment (such AWS, GCP, Azure, or Modal)
• Experience in fast moving AI, ML or high growth environments, such as startups, research labs, or AI-driven product teams
• Bachelor’s, Master’s, or PhD in Computer Science, Mathematics or a related field

Desirable

• As an ML Engineer, you’ll help us index, benchmark, and evaluate training datasets at scale. Your expertise with data, AI and ML training methodologies and evaluation techniques will advance the state of the art for developing AI
• Designing and developing benchmarks that allow our customers to understand their value of data for training ML (quantifying dataset quality and biases)
• Deploy these benchmarks by implementing end-to-end data evaluation pipelines to be run on different datasets and ML models
• Tools to visualise, analyze, and understand the attributes of datasets based on the evaluations
• Develop ML models to transform, clean and understand data
• Collaborating with cross-functional teams, including operations, software engineering, and product management, to integrate data evaluation tools and insights into product development
• Build scalable, high performance systems to support our AI data marketplace
• Optimise data pipelines to improve data discovery and quality evaluation
• Maintain cloud based ML infrastructure and ensure system reliability
• Work cross functionally to translate business needs into technical solutions
• Advocate for pragmatic, simple solutions over unnecessary complexity
• Communicate trade-offs and engineering decisions clearly
• Help to define the engineering culture and best practices as we grow
• Improve developer experience by building internal tools and automation
• Ensure AI licensing remains fair, transparent, and responsible