招聘

Applied Scientist - Multimodal Foundation Models & Robotics
Switzerland, Zürich, Zürich
·
On-site
·
Full-time
·
2mo ago
必备技能
Python
C++
Deep Learning
Machine Learning
Overview:
The Spatial AI Lab is part of the Applied Sciences Group, a Microsoft research and development organization dedicated to creating next-generation human-computer interaction technologies leveraging the most recent AI developments and exploring new hardware capabilities and device form-factors. Our team of scientists and engineers has strong expertise in computer vision and multi-modal AI, with a particular focus on spatial and embodied AI.
As a Scientist - Multimodal Foundation Models & Robotics on our growing team, you will conduct research at the intersection of large-scale generative modeling and embodied AI, with a focus on robotics. Your primary focus will be on building the core intelligence for a new generation of agents, training the multimodal foundation models that empower them to perceive complex environments, reason about tasks, and act seamlessly across both the physical and digital worlds. This opportunity will allow you to deepen your expertise in training embodied foundation models, deploying algorithms on robotic hardware and large-scale AI systems, and contribute to our pioneering research through publications and collaborations with partners like ETH Zurich.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
- Responsibilities- Design and implement novel foundation models and algorithms for general-purpose embodied agents;
- Implement high-performance machine-learning pipelines and optimize data and learning stacks for scalability, efficiency, and performance.
- Optimize and deploy AI models on robot hardware;
- Collaborate across Microsoft research and engineering teams to transition cutting-edge research into real-world impact.
- Drive the team’s rapid progress, with success measured by both the advancement of internal capabilities and impactful contributions to the scientific community
- Opportunity to collaborate with top academic partners like ETH Zurich to advance pioneering research at scale, with opportunities to co-author work in top-tier venues, present at workshops, and mentor students.
Qualifications Preferred Qualifications:
- A PhD in Robotics, Machine Learning, or a related field, OR 3+ years of relevant industry experience.
- Demonstrated coding, debugging, and engineering skills in programming languages such as Python or C++.
- Hands-on experience with modern deep learning frameworks (e.g. Pytorch/Tensorflow/Jax).
- Self-motivated team-player, problem solver, and keen to learn.
- Ability to present complex technical concepts to a diverse audience.
Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Quaifications:
- Experience in one or more of the following areas:Foundation Models: hands-on training experience in at least one of the following topics: LLMs; Large vision-language models (VLMs);
- Video generative models and diffusion algorithms;
- or action-based transformers and Vision Language Action models (VLAs).
- Large-Scale ML Systems: Experience with large scale machine learning compute systems.
- Robotics:Hands-on training experience in robot learning techniques, such as reinforcement learning, imitation learning as well as classical control methods
- Solid understanding of robot kinematics, dynamics and sensors
- Familiarity with control algorithms such as PID, model predictive control (MPC), and whole-body control.
- Track record of impact, either via first author research publications at top-tier machine learning or robotics conferences (CoRL, RSS, NeurIPS, ICML, ICLR, CVPR), or via contributions to successful industry initiatives.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
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关于Microsoft

Microsoft
PublicMicrosoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.
10,001+
员工数
Redmond
总部位置
$3000B
企业估值
评价
3.8
5条评价
工作生活平衡
4.1
薪酬
4.3
企业文化
3.4
职业发展
3.2
管理层
3.0
65%
推荐给朋友
优点
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
缺点
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
薪资范围
5,620个数据点
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Mid/L4 · Applied Science
1份报告
$234,166
年薪总额
基本工资
$180,128
股票
-
奖金
-
$234,166
$234,166
面试经验
1次面试
难度
4.0
/ 5
时长
14-28周
体验
正面 0%
中性 0%
负面 100%
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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