Senior Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Spearhead the development of cutting-edge machine learning and AI models for marketplace algorithms, encompassing search ranking, recommendation, and matching solutions.

• Create and structure robust MLOps practices to ensure smooth deployment and scalability of machine learning models, including self-hosted large language models.

• Streamline model training and post-training processes, which includes fine-tuning, RLHF/preference alignment, and distillation.

• Enhance model runtime performance and reduce inference costs.

• Oversee the complete MLOps lifecycle from data pipelines and experiment tracking through CI/CD, model registry, monitoring, and rollback procedures.

• Establish and manage evaluation frameworks for deep learning and ML systems, incorporating offline metrics, online experimentation, guardrail metrics, and LLM-specific assessments.

• Collaborate effectively with engineers, members of the ML infrastructure team, data scientists, and product managers to build scalable machine learning systems.

• Participate in the entire development cycle from ideation to deployment.

• Aid in formulating long-term technical vision and suggesting team roadmaps.

• Design and execute new products and features while enhancing existing offerings with machine learning capabilities.

• Mentor junior team members and promote ongoing learning and technical excellence.

• Lead knowledge-sharing initiatives focused on advanced machine learning techniques.

⛳️ Requirements

• Master's or Ph.D. degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.

• Over 4 years of experience in data science and machine learning, preferably within the tech industry and marketplace settings.

• Practical experience with post-training and self-hosting open-weight LLMs, including fine-tuning, quantization, and serving infrastructure like vLLM and SGLang.

• Profound understanding of evaluation metrics for both deep learning and classical machine learning.

• Comprehensive fluency in the MLOps lifecycle, covering data versioning, feature stores, CI/CD for ML, model monitoring/observability, and retraining pipelines.

• Knowledge of large-scale distributed application architecture, design, implementation, and performance optimization.

• Capability to steer the roadmap and direction of scalable, production-quality systems while effectively communicating across Product and Engineering teams.

• Practical understanding of advanced machine learning algorithms and deep learning applications for search systems, information retrieval, and ranking algorithms.

• Proficiency in SQL, Python, and ML frameworks such as TensorFlow and PyTorch, along with strong coding abilities.

• Exceptional communication skills, enabling the explanation of complex technical concepts to non-technical stakeholders.

🏝️ Benefits

• Competitive year-end performance bonus.

• Equity package.

• Comprehensive medical, dental, and vision coverage.

• Flexible vacation policy.

• Pet discount plans.

• Retirement plan with company match (401K).

• Opportunity to collaborate with sharp, motivated colleagues on unique challenges.