招聘

Applied Scientist Intern: Differentially Private Video Synthesis
United Kingdom, Cambridgeshire, Cambridge
·
On-site
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Internship
·
1w ago
Overview
Join a strong research team advancing high-fidelity synthetic data generation pipelines. You will work on cutting-edge research in differentially private synthetic data generation, with a particular focus on video generation. Opportunities include advancing our existing work on Private Evolution and Differentially Private Reinforcement Fine-tuning, designing new innovative algorithms for DP synthetic data, and applying them to high-impact real-world projects.
Responsibilities-
Advance the research: Develop cutting-edge algorithms for differentially private video synthesis, including mechanisms, training objectives, and/or optimization strategies that improve fidelity, stability, and downstream utility.
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Own experiments end-to-end: Define hypotheses, build rigorous evaluation protocols for synthetic video data (e.g., classification, detection, pose/landmarks, segmentation, tracking), run ablations, and analyze results at scale.
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Collaborate broadly: Work closely with researchers and engineers to co-design algorithms, benchmarks, and experimental setups aligned with broader program goals.
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Communicate impact: Present findings clearly, contribute to internal reports, and—where appropriate—author submissions to top-tier research venues.
Qualifications:
Rquired/Minimum Qualifications:
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Currently enrolled in a PhD program (senior stage), or a Master’s student with a strong publication record.
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Peer reviewed publications or preprints in top venues (e.g., ICCV, CVPR, SIGGRAPH, ECCV, NeurIPS, ICLR) or equivalent evidence of research impact.
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Research experience in image and video synthesis and deep learning, including training, fine tuning, and evaluation of modern vision models.
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Strong skills in experimental design, quantitative analysis, and clear scientific communication.
Preferred/Additional Qualifications:
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Experience with models or agents for video generation.
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Knowledge of human-centric tasks (pose/landmarks, tracking, activity recognition, face/hand analysis).
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Demonstrated ability to collaborate in multidisciplinary teams and to drive projects with minimal supervision.
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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About Microsoft
Reviews
3.8
5 reviews
Work Life Balance
4.1
Compensation
4.3
Culture
3.4
Career
3.2
Management
3.0
65%
Recommend to a Friend
Pros
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
Cons
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
Salary Ranges
5,571 data points
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Mid/L4 · Data and Applied Scientist
0 reports
$202,099
total / year
Base
$149,342
Stock
$32,252
Bonus
$20,505
$139,572
$301,212
Interview Experience
7 interviews
Difficulty
3.7
/ 5
Duration
14-28 weeks
Offer Rate
14%
Experience
Positive 14%
Neutral 29%
Negative 57%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Technical Interview
5
Onsite/Virtual Interviews
6
Final Round
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
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