招聘
必备技能
PyTorch
TensorFlow
Machine Learning
Job Description:
Main Responsibilities:
We are seeking a highly skilled TITLE to join our team and drive innovation in AI-powered drug evaluation and development. This role focuses on building and optimizing advanced AI systems, including large language models (LLMs) and molecular generation and property prediction models. The ideal candidate will combine deep technical expertise with domain knowledge in pharmaceutical R&D, enabling rapid iteration and deployment of cutting-edge solutions.
Qualifications:
-
PhD or Master with equivalent practical experience in Computational Chemistry, Computational Biology, Computer Science, Machine Learning, or a related technical field.
-
Experience in disease and target research is highly desired.
-
Experience in drug discovery and development is highly desired.
-
Deep understanding and experience of LLM model architecture, training, and application in literature mining and scientific insights generation, including deployment, post-training, reasoning, tool-use, alignment, agents, as well as reinforcement learning and fine-tuning.
-
Deep understanding and experience of graph neural networks, generative models and other deep learning models for drug discovery applications, including structure prediction, molecule design, property prediction, etc.
-
Experience using ML frameworks such as Py Torch, Tensor Flow, JAX, and scientific software such as Num Py, Sci Py, or Pandas.
-
Iterate collaboratively with scientists and domain experts in biology and chemistry. Experience of integrating scientific insights and data into AI models.
-
Ability to learn quickly in a fast-paced environment.
-
Excellent communication skills for cross-disciplinary collaboration.
Required Skills:
Academic Conferences, Academic Conferences, Bioanalytical Analysis, Bioanalytical Techniques, Biochemical Assays, Bioinformatics, Biomarker Development, Clinical Judgment, Computational Biology, Computational Chemistry, Computational Neuroscience, Computer Science, Creativity, Detail-Oriented, Drug Development, Drug Discovery Process, Ethical Compliance, Immunohistochemistry (IHC), In Vivo Pharmacology, Language Models, Large Language Models (LLMs), Leadership, Management Process, Mass Spectrometry Analysis, Molecular Biology {+ 5 more}
Preferred Skills:
Current Employees apply HERE:
Current Contingent Workers apply HERE:
Search Firm Representatives Please Read Carefully Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.
Employee Status:
Regular
Relocation:
No relocation
VISA Sponsorship:
No
Travel Requirements:
No Travel Required:
Flexible Work Arrangements:
Not Applicable
Shift:
Not Indicated
Valid Driving License:
No
Hazardous Material(s):
no
Job Posting End Date:
06/30/2026
A job posting is effective until 11: 59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.
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关于Merck

Merck
PublicMultinational pharmaceutical company.
10,001+
员工数
Rahway
总部位置
$208B
企业估值
评价
4.3
10条评价
工作生活平衡
3.8
薪酬
3.2
企业文化
4.5
职业发展
3.5
管理层
4.0
78%
推荐给朋友
优点
Supportive management and colleagues
Excellent health benefits and retirement plans
Flexible working hours and remote work options
缺点
High-pressure and demanding work environment
Limited career advancement opportunities
Pay issues for entry-level positions
薪资范围
2,504个数据点
Intern
Intern · Process Analysis Intern
1份报告
-
年薪总额
基本工资
-
股票
-
奖金
-
面试经验
6次面试
难度
3.0
/ 5
时长
14-28周
录用率
17%
体验
正面 0%
中性 67%
负面 33%
面试流程
1
Application Review
2
HR Screen
3
Hiring Manager Interview
4
Panel Interview
5
Background Check
6
Offer
常见问题
Behavioral/STAR
Past Experience
Technical Knowledge
Culture Fit
Case Study
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