Jobs
Benefits & Perks
•Equity
•Healthcare
•401(k)
•Equity
•Healthcare
•401k
Required Skills
Python
Reinforcement learning
Large language models
Distributed training
We are seeking a technically skilled GenAI scientist to join our team focused on Large Language Model (LLM) agents and model post-training, with a particular emphasis on reinforcement learning (RL). This role will be close to product applications and user impact, requiring full-stack knowledge.
Language Research Scientist Responsibilities:
- Design, implement, and optimize LLM-based agents for a variety of applications, leveraging the latest advances in generative AI
- Apply reinforcement learning algorithms to improve LLM performance, safety, and alignment
- Integrate models and orchestrations in production
- Collaborate with cross-functional teams (research, engineering, product) to deploy and evaluate LLM agents in real-world scenarios
- Analyze and interpret experimental results, iterate on model architectures, and drive continuous improvement
- Contribute to the broader AI/ML community at Meta through knowledge sharing, code reviews, and technical mentorship
- Lead and contribute to research and development of post-training methods, including RLHF (Reinforcement Learning from Human Feedback), reward modeling, and other feedback-based approaches
Minimum Qualifications:
- Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Generative AI, or a relevant technical field
- Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
- Good programming skills in Python and familiarity with large-scale distributed training
- Familiarity to learn new programming languages quickly
- Can design, implement, and evaluate RL algorithms in production or research settings
- Problem-solving, communication, and collaboration skills
Preferred Qualifications:
- Experience with RLHF, reward modeling, or other LLM post-training techniques
- Experience working in cross-functional teams
- Track record of publications or contributions to open-source projects in LLMs, RL, or related areas
- Familiarity with safety, alignment, and evaluation challenges in generative AI
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and Whats App further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today-beyond the constraints of screens, the limits of distance, and even the rules of physics.
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
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About Meta

Meta
PublicA social technology company that enables people to connect, find communities, and grow businesses.
10,001+
Employees
Menlo Park
Headquarters
$800B
Valuation
Reviews
3.4
26 reviews
Work Life Balance
2.3
Compensation
4.2
Culture
2.8
Career
3.1
Management
2.1
45%
Recommend to a Friend
Pros
Excellent compensation and benefits
Smart and talented colleagues
Fast-paced and challenging work environment
Cons
Frequent layoffs and job insecurity
Poor leadership and management accountability
High stress and competitive work environment
Salary Ranges
40,175 data points
Mid/L4
Mid/L4 · Data Scientist
3,113 reports
$284,667
total / year
Base
$179,458
Stock
$79,981
Bonus
$25,228
$193,897
$434,902
Interview Experience
6 interviews
Difficulty
4.2
/ 5
Duration
21-35 weeks
Offer Rate
17%
Experience
Positive 17%
Neutral 17%
Negative 66%
Interview Process
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Coding Interviews
6
System Design Interview
7
Behavioral Interview
8
Final Loop/Hiring Manager Round
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Live Coding
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