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Sr Machine Learning Engineer, AI Research
Rol remoto de Machine Learning Engineering con fit claro de ubicación del candidato.
Publicado9 jul 2026
Países elegibles1 país aceptado
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Sr Machine Learning Engineer, AI Research
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
As An Active Member Of Our Team, You Will...
- Design, train, and evaluate machine learning models across a range of research and applied AI initiatives
- Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements
- Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems
- Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation
- Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation
- Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team
- Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them
- This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones
If You've Got It - We Want It
- Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 4+ years of industry or research experience (Master's or PhD a plus)
- Deep hands-on experience training and evaluating ML models, including language models
- Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights & Biases, Kubeflow, or similar)
- Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques
- Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize
- Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders
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