研究工程师 5/6 - AI 会员系统
Research Engineer 5/6 – AI for Member Systems
在Netflix，我们的使命是娱乐全世界。我们正在书写下一章——突破叙事、全球粉丝和将不可想象变为现实的边界。我们是一支梦想团队，痴迷于探索创意、直觉和尖端技术融合时带来的不适感与兴奋感。加入我们，成为未来的一部分。
随着Netflix的持续增长，我们有更多机会提升个性化系统和算法。我们正在寻找一位充满热情且才华横溢的研究工程师，加入我们的会员系统AI团队。在这个职位上，你将运用机器学习和软件工程的专业知识，设计、开发并扩展推动Netflix体验的解决方案。
## **主要职责：**
- 与跨职能团队合作，包括研究人员、工程师、数据科学家和产品经理，开发和实现提升个性化、推荐和会员体验的机器学习算法。
- 创建可扩展、生产就绪的机器学习解决方案，将算法从初步概念推进到Netflix大规模实时系统中的部署。
- 优化机器学习模型的性能和可扩展性，确保它们能够应对我们全球会员多样化的品味和行为。
- 设计并执行离线实验和A/B测试，验证算法变更对关键业务指标的影响。
- 为我们的机器学习基础设施和工具的持续改进做出贡献，确保我们保持行业前沿的实践。
- 参与持续的学习和发展，紧跟机器学习和软件工程的最新进展。
## **我们寻找的人：**
- 在工业环境中应用机器学习5年以上，有交付显著成果的记录。
- 计算机科学、统计学或相关领域的博士或硕士学位
- 精通机器学习算法和框架，有在生产环境中训练、调优和部署模型的实际经验。
- 出色的Python软件设计和开发技能，以及Scala、Java、C++或C#的经验
- 在以下一个或多个应用领域有经验：推荐系统、个性化、长期奖励建模、强化学习、Transformer、大规模语言模型、LLM评估、RLHF奖励建模/对齐
- 优秀的沟通能力，包括强大的人际交往能力
查看英文原文
At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.
As Netflix continues to grow, so do the opportunities to enhance our personalization systems and algorithms. We're looking for a passionate and talented Research Engineer to join our Al for Member Systems. In this role, you will apply your expertise in machine learning and software engineering to design, develop, and scale solutions that power the Netflix experience.
## **Key Responsibilities:**
- Collaborate with cross-functional teams, including researchers, engineers, data scientists, and product managers, to develop and implement machine learning algorithms that improve personalization, recommendations, and member experiences.
- Create scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment in Netflix's large-scale, real-time systems.
- Optimize the performance and scalability of machine learning models, ensuring they can handle the diverse tastes and behaviors of our global member base.
- Design and conduct offline experiments and A/B tests to validate the impact of algorithmic changes on key business metrics.
- Contribute to the ongoing improvement of our ML infrastructure and tooling, ensuring that we stay at the cutting edge of industry practices.
- Engage in continuous learning and development, staying up-to-date with the latest advances in machine learning and software engineering.
## **What we are looking for:**
- 5+ years of experience in applying machine learning in an industrial setting, with a track record of delivering impactful results.
- PhD or Masters in Computer Science, Statistics, or a related field
- Expertise in machine learning algorithms and frameworks, with hands-on experience in training, tuning, and deploying models in production environments.
- Excellent software design and development skills in Python along with Scala, Java, C++, or C#
- Experience in one or more of the following applied fields: Recommendations, Personalization, Long-term Reward Modeling, Bandits, Transformers, Large-Scale Language Models, LLM evaluation, RLHF reward modeling/alignment
- Great interpersonal skills including strong written and verbal communication
## **Preferred Qualifications:**
- Experience building or enhancing personalization systems, search engines, or similar large-scale machine learning applications.
- Background in neural networks, natural language processing, or causal inference
- Contributions to open-source projects in machine learning or related fields.
- Experience working with cross functional teams
## **Links:**
- [Netflix Research site](https://research.netflix.com/)
- [Our long term business view](https://ir.netflix.net/ir-overview/long-term-view/default.aspx)
**_NOTE: This job posting is inclusive of a variety of positions within our AI for Member Systems (AIMS) Engineering group. Based on your background, expertise and interests, we will route you to the appropriate team(s). All teams may not be hiring at the same time._**
Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $600,000.00 - $1,066,000.00. This compensation range will vary based on location.
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits [here](https://jobs.netflix.com/work-life-philosophy).
Netflix is a unique culture and environment. Learn more [here](https://jobs.netflix.com/culture).
[Inclusion](https://about.netflix.com/en/inclusion) is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Job is open for no less than 7 days and will be removed when the position is filled.