Applied Machine Learning (ML) Engineer, Machine Learning (ML) and Artificial Int
应用机器学习 (ML) 工程师，机器学习和人工智能 (AI) 要求，从家工作的应用机器学 (ML)工程师将构建并改进我们的 AI-原生应用程序的机器学习系统。你将在分类，检索，排名，个性化和评估上工作，从模型开发到生产部署的所有权。这个职位是为那些喜欢解决实用机器学习问题，提高模型质量和运输可靠系统使用真正用户的人工学习 (ML).需要机器学习与人工智能经验。该职位是100%远程。必须愿意接受60分钟的编码评估。应用机器学习 (ML) 工程师责任：·构建和改进机器学习模型进行分类，排名，检索和个性化。·开发高效的分类仪并优化模型精度，准确性，延迟和成本。·通过嵌入式，混合搜索和重新排序来提高搜索相关性和文本检索。·建立从用户行为中学到个人化的模型，同时尊重隐私。·制定评估管道，标签数据集和指标以衡量模型质量的模型。·充分支持数据时调整和适应现有模型。· 建立和维护机器学习 (ML) 培训，推断和部署管道。· 对生产问题进行调试，监测模型性能和基于现实世界的反的船舶改进。应用机器学习的工程师结果：· 在生产中使用的机器学习模型达到预期准确性，相关性，延迟和可靠性目标。· 分类和排名改善为模型质量带来可衡量的收益。·评估管道识别退缩并支持可靠的模型发布。· 培训和推断以及数据管道是强大，可复制且可维持的。· 模型改进是由现实世界中的反和可衡的结果驱动的。·与工程和产品团队有效合作，以提供可靠的 ML 驱动功能。应用机器学习 (ML) 工程师资格：·需要机器学习的经验。·需要人工智能 (AI) 的经验。•在机器学习和现代神经架构方面有强大的基础。·实践开发和部署机器学习模型生产。·分类，ra 方面的经验。
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Applied Machine Learning (ML) Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home
The Applied Machine Learning (ML) Engineer will build and improve Machine Learning (ML) systems that power our AI-native applications. You will work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment. This role is for engineers who enjoy solving practical Machine Learning (ML) problems, improving model quality and shipping reliable systems used by real users. Machine Learning (ML) and Artificial Intelligence (AI) experience are required. This position is 100% Remote.
MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT.
Applied Machine Learning (ML) Engineer Responsibilities:
- Build and improve Machine Learning (ML) models for classification, ranking, retrieval and personalization.
- Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
- Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
- Build personalization models that learn from user behavior while respecting privacy.
- Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
- Fine-tune and adapt existing models when supported by sufficient data.
- Build and maintain Machine Learning (ML) training, inference and deployment pipelines.
- Debug production issues, monitor model performance and ship improvements based on real-world feedback.
Applied Machine Learning (ML) Engineer Outcomes:
- Machine Learning (ML) models in production meet expected accuracy, relevance, latency and reliability targets.
- Classification and ranking improvements deliver measurable gains in model quality.
- Evaluation pipelines identify regressions and support reliable model releases.
- Training, inference and data pipelines are robust, reproducible and maintainable.
- Model improvements are driven by real-world feedback and measurable results.
- Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.
Applied Machine Learning (ML) Engineer Qualifications:
- Machine Learning (ML) experience is required.
- Artificial Intelligence (AI) experience is required.
- Strong foundations in Machine Learning (ML) and modern neural architectures.
- Hands-on experience building and deploying Machine Learning (ML) models in production.
- Experience in classification, ranking, retrieval, recommendation systems or personalization.
- Understanding of model evaluation, experimentation and performance metrics.
- Comfortable writing production-quality code and working with real-world datasets.
- Able to balance model quality, latency, cost and reliability.
- Strong ownership, problem-solving skills
- Tech Stack: Embeddings, vector search and ranking models, Production Machine Learning (ML) systems and inference pipelines, Python, and PyTorch/JAX.
Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.
Keywords: San Francisco CA Jobs, Applied Machine Learning (ML) Engineer, AI, Artificial Intelligence, Datasets, Embedding, JAX, Machine Learning (ML) Classification, Machine Learning (ML) Inference Pipelines, Machine Learning (ML) Models, Machine Learning (ML) Performance Metrics, Machine Learning (ML) Personalization, Machine Learning (ML) Ranking, Machine Learning (ML) Recommendation Systems, Machine Learning (ML) Retrieval, ML, Machine Learning, Modern Neural Architectures, Production Machine Learning (ML) Systems, Python, PyTorch, Vector Search and Ranking Models, Work From Home, Remote, California Recruiters, IT Jobs, California Recruiting
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Originally posted on Himalayas