Machine Learning Engineer

Role overview A machine learning engineering role focused on building and productionizing data-driven models that power real business applications. The work spans the full model lifecycle—from architecture selection and training through evaluation, deployment, and ongoing refinement—at a scale that involves billions of data points. The team is oriented around keeping its AI stack state of the art, with a particular emphasis on advancing language classification.

Responsibilities - Design, develop, and evaluate ML models end to end, including prototyping, training, validation, and production deployment - Apply and adapt statistical and ML techniques such as Bayesian models, deep neural networks, and numerical optimization to business problems - Assess emerging model architectures, fine-tuning strategies, and open-weight releases, and integrate promising ones into the production stack - Operate and maintain models after launch, including monitoring performance, updating models, and addressing drift or regressions - Use large-scale compute resources efficiently to train and iterate on models against massive datasets - Collaborate on advancing language classification capabilities, translating research insights into deployed systems

Requirements - Hands-on experience designing, training, and deploying machine learning models in production environments - Strong knowledge of modern ML methods, including deep neural networks and Bayesian approaches - Familiarity with optimization techniques commonly used to train large models - Experience working with large-scale datasets and distributed or scalable compute infrastructure - Ability to evaluate model architectures, fine-tuning methods, and emerging research for practical adoption - Solid engineering practices for reproducibility, experimentation, and reliable deployment of ML systems