机器学习工程师负责人
Lead Machine Learning Engineer
Janea Systems（美国）是由来自全球的顶尖软件工程专家和解决方案创新者组成的一支充满活力的团队。从内核到云，我们为财富500强公司提供高影响力的软件开发服务
我们正在寻找一位才华横溢的机器学习首席工程师加入我们快速发展的咨询团队。在这个职位上，你将领导为企业客户设计、开发和部署可扩展的机器学习和AI系统
你将在机器学习、分布式系统和生产软件工程的交汇点工作，帮助将前沿的ML研究转化为稳健、生产级的解决方案。作为首席ML工程师，你将指导ML架构决策，指导工程师，并与跨职能团队合作，在多个行业交付高影响力AI解决方案
地点
全远程/需居住在欧洲
薪酬
薪资
工作时间
全职/弹性工作时间
汇报对象
工程总监
所属部门
工程团队
申请此职位需具备以下资格：
- 8年以上软件工程、机器学习工程或应用AI领域的工作经验
- 3年以上技术领导或指导工程团队的经验
- 在生产环境中开发和部署机器学习模型的丰富经验
- 熟练使用PyTorch、TensorFlow或其他类似ML框架
- 设计可扩展ML系统和数据管道的经验
- 精通Python编程，并有后端或分布式系统经验
- 使用云平台（AWS、Azure或GCP）的经验
- 使用容器化和部署技术（Docker、Kubernetes）的经验
- 对MLOps实践和模型生命周期管理有深入理解
- 优秀的解决问题能力，能够在远程环境中独立工作
- 优秀的英文书面和口语沟通能力
- 计算机科学、机器学习、人工智能或相关领域的学位
理想的候选人还应具备：
- 使用LLMs、生成式AI系统或深度学习架构的经验
- 构建ML平台或内部AI工具的经验
- 熟悉分布式训练和大规模数据处理框架
- 使用特征存储、模型监控和ML可观测性工具的经验
- 交付ML项目的经验
查看英文原文
Janea Systems (USA) is a dynamic team of the best & brightest software engineering specialists and solutions innovators, from around the world. From kernel to cloud, we provide high-impact software development services to Fortune 500 companies.
We are seeking an exceptionally talented Lead Machine Learning Engineer to join our rapidly growing consulting team. In this role, you will lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients.
You will work at the intersection of machine learning, distributed systems, and production software engineering, helping transform cutting-edge ML research into robust, production-grade solutions. As a Lead ML Engineer, you will guide ML architecture decisions, mentor engineers, and collaborate with cross-functional teams to deliver high-impact AI solutions across multiple industries.
Location
Fully Remote/ European Residence required
Compensation
Salary
Work Schedule
Full time/ Flexible working hours
Reports to
Head of Engineering
Member of
Engineering Team
To be considered for this position, you must have the following qualifications:
- 8+ years of experience in software engineering, machine learning engineering, or applied AI.
- 3+ years of experience in technical leadership or mentoring engineering teams.
- Strong experience developing and deploying machine learning models in production environments.
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or similar.
- Experience designing scalable ML systems and data pipelines.
- Strong programming skills in Python and experience with backend or distributed systems.
- Experience with cloud platforms (AWS, Azure, or GCP).
- Experience with containerization and deployment technologies (Docker, Kubernetes).
- Strong understanding of MLOps practices and model lifecycle management.
- Excellent problem-solving skills and ability to work independently in remote environments.
- Strong written and spoken English communication skills.
- Degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Ideal candidates will also have:
- Experience working with LLMs, generative AI systems, or deep learning architectures.
- Experience building ML platforms or internal AI tooling.
- Familiarity with distributed training and large-scale data processing frameworks.
- Experience with feature stores, model monitoring, and ML observability tools.
- Experience delivering ML solutions in client-facing consulting environments.
Why join Janea? Because world-class talent deserves world-class opportunities. What we offer:
- Competitive compensation with benefits, paid vacation, and sick leave.
- The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineeringchallenges.
- Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.
- An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.
- Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.
Originally posted on Himalayas