Staff Machine Learning Engineer, AI Research

Description:

• Design, train, and evaluate machine learning models for research and applied AI initiatives.
• Run rapid experiments to test hypotheses and identify model improvements.
• Collaborate with researchers, engineers, development partners, and stakeholders to translate academic advances into production-ready systems.
• Build and maintain ML pipelines for data ingestion, feature engineering, model training, and evaluation.
• Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation.
• Track experiment results, document findings, and share learnings with the broader team.
• Stay current with the latest ML and AI research and identify opportunities to apply new methods.
• Participate in stand-by, on-call, or off-hours support during critical research or deployment milestones.

Requirements:

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field.
• 5+ years of industry or research experience.
• Master's degree or PhD is 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 such as MLflow, Weights & Biases, Kubeflow, or similar.
• Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques.
• Ability to move fast without sacrificing rigor, including knowing when to prototype and when to productionize.
• Excellent communication skills for presenting experimental results to technical and non-technical stakeholders.

Benefits:

• Salary range of $230,000 to $275,000, depending on geographic location and experience.
• Comprehensive health, dental, vision, short-term disability, and life insurance coverage.
• Paid holidays and paid time off.
• Fertility treatment benefit.
• 401(k) plan.
• Equity package.
• Eligibility for a discretionary company-wide bonus.
• Remote-first work environment.

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