OPT Applied Scientist Jobs
Applied Scientist roles on OPT sit squarely within specialty occupation territory, which makes them strong candidates for H-1B visa sponsorship down the line. Most positions require a master's or PhD in computer science, statistics, or a related field. STEM OPT gives you up to three years of authorized work, enough runway to move through the sponsorship process.
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Get Access To All JobsResponsibilities
We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company. Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.
About The Team
Our Trust and Safety team is fast growing and responsible for building machine learning models and systems to protect our users from the impact of negative content. Our mission is to protect billions of users and publishers across the globe every day. We embrace state-of-the-art machine learning technologies and scale them to moderate the tremendous amount of data generated on the platform. With our team's continuous efforts, TikTok can provide the best user experience and bring joy to everyone in the world.
Project Overview, Challenges & Value
With the rapid development of AIGC and the globalization of content ecosystems, content moderation faces three major challenges: evolving policies, surging complexity in multilingual and multimodal content, and upgraded generative adversarial attacks. The traditional "perception → classification" paradigm has reached its limit. This topic focuses on two frontier directions:
(1) Multimodal moderation foundation model: We study large-scale MoE architecture training and routing optimization, cross-modal alignment and reasoning for multimodality (text/image/video/audio), Unified Understanding & Generation, and high-quality synthetic data generation for moderation scenarios (self-play / adversarial augmentation).
(2) Agentic moderation system: Drawing on advanced agent learning paradigms, it uses reinforcement learning to enhance the agent’s multi-step decision-making capabilities. It dynamically builds moderation context and integrates a flexible tool ecosystem, enabling autonomous planning, tool collaboration, and interpretable closed-loop reasoning. This drives a paradigm shift from passive classification to proactive intelligent decision-making in moderation.
Key Challenges Include
- MoE-based multimodal safety foundation model: training stability and routing optimization for large-scale sparse MoE, cross-modal token alignment, and unified architecture design for understanding and generation
- RL-driven agentic decision-making: end-to-end training of agent multi-step reasoning and tool-call strategies based on GRPO/PPO, overcoming bottlenecks in sample efficiency and training stability
- Context engineering and tool collaboration: dynamic context assembly, MCP-based tool ecosystem construction, multi-source heterogeneous evidence fusion, and GraphRAG strategy retrieval
- Generalization and adversarial robustness: generalization across 200+ languages/strategies, adversarial detection of AIGC content, and design of multi-dimensional reward signals for few-shot scenarios
Project Value
- Technological leadership: The integration of RL, Agentic, and multimodal foundation models represents the frontier of AI today. This topic pioneers their application to large-scale content moderation scenarios, with unique advantages in data volume and real-world feedback loops that are impossible to reproduce in pure academic settings.
- Business value: Serving content safety for billions of users globally and driving the evolution of moderation from dependence on humans and external APIs towards fully automated agentic moderation. This can directly reduce costs by hundreds of millions of US dollars while improving moderation consistency and response speed.
- Industry leadership: Mature RL-driven agentic moderation systems do not yet exist in the industry. This topic could hopefully define the technological paradigm for this direction and produce research outcomes with significant industry influence.
Qualifications
Minimum Qualifications
- Individuals who are completing or have recently completed a PhD degree in Computer Science, Data Science, Artificial Intelligence, or a related field
- Strong understanding of cutting-edge LLM research (e.g., long context, multi modality, alignment research, agent ecosystem, etc.) and possess practical expertise in effectively implementing these advanced systems as a plus
- Proficiency in programming languages such as Python, Rust, or C++ and a track record of working with deep learning frameworks (e.g., pytorch, deepspeed, megatron, vllm, etc.).
- Strong understanding of distributed computing framework & performance tuning and verification for training/finetuning/inference; Being familiar with PEFT, RL, MoE, CoT or Langchain is a plus.
Preferred Qualifications
- Excellent problem-solving skills and a creative mindset to address complex AI challenges. Demonstrated ability to drive research projects from idea to implementation, producing tangible outcomes.
- Published research papers or contributions to the LLM community would be a significant plus.
- Experience with inference tuning and Inference acceleration. Have a deep understanding of GPU and/or other AI accelerators, experience with large scale AI networks, pytorch 2.0 and similar technologies.
- Experience with evaluation of AI systems, LLM application & agent development is desirable.
Job Information
【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $212800 - $450000 annually. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units. Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure). The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
- Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
- Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
- Exercising sound judgment.
About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy – a mission we work towards every day. We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity & Inclusion
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
TikTok Accommodation
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request
Responsibilities
We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company. Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.
About The Team
Our Trust and Safety team is fast growing and responsible for building machine learning models and systems to protect our users from the impact of negative content. Our mission is to protect billions of users and publishers across the globe every day. We embrace state-of-the-art machine learning technologies and scale them to moderate the tremendous amount of data generated on the platform. With our team's continuous efforts, TikTok can provide the best user experience and bring joy to everyone in the world.
Project Overview, Challenges & Value
With the rapid development of AIGC and the globalization of content ecosystems, content moderation faces three major challenges: evolving policies, surging complexity in multilingual and multimodal content, and upgraded generative adversarial attacks. The traditional "perception → classification" paradigm has reached its limit. This topic focuses on two frontier directions:
(1) Multimodal moderation foundation model: We study large-scale MoE architecture training and routing optimization, cross-modal alignment and reasoning for multimodality (text/image/video/audio), Unified Understanding & Generation, and high-quality synthetic data generation for moderation scenarios (self-play / adversarial augmentation).
(2) Agentic moderation system: Drawing on advanced agent learning paradigms, it uses reinforcement learning to enhance the agent’s multi-step decision-making capabilities. It dynamically builds moderation context and integrates a flexible tool ecosystem, enabling autonomous planning, tool collaboration, and interpretable closed-loop reasoning. This drives a paradigm shift from passive classification to proactive intelligent decision-making in moderation.
Key Challenges Include
- MoE-based multimodal safety foundation model: training stability and routing optimization for large-scale sparse MoE, cross-modal token alignment, and unified architecture design for understanding and generation
- RL-driven agentic decision-making: end-to-end training of agent multi-step reasoning and tool-call strategies based on GRPO/PPO, overcoming bottlenecks in sample efficiency and training stability
- Context engineering and tool collaboration: dynamic context assembly, MCP-based tool ecosystem construction, multi-source heterogeneous evidence fusion, and GraphRAG strategy retrieval
- Generalization and adversarial robustness: generalization across 200+ languages/strategies, adversarial detection of AIGC content, and design of multi-dimensional reward signals for few-shot scenarios
Project Value
- Technological leadership: The integration of RL, Agentic, and multimodal foundation models represents the frontier of AI today. This topic pioneers their application to large-scale content moderation scenarios, with unique advantages in data volume and real-world feedback loops that are impossible to reproduce in pure academic settings.
- Business value: Serving content safety for billions of users globally and driving the evolution of moderation from dependence on humans and external APIs towards fully automated agentic moderation. This can directly reduce costs by hundreds of millions of US dollars while improving moderation consistency and response speed.
- Industry leadership: Mature RL-driven agentic moderation systems do not yet exist in the industry. This topic could hopefully define the technological paradigm for this direction and produce research outcomes with significant industry influence.
Qualifications
Minimum Qualifications
- Individuals who are completing or have recently completed a PhD degree in Computer Science, Data Science, Artificial Intelligence, or a related field
- Strong understanding of cutting-edge LLM research (e.g., long context, multi modality, alignment research, agent ecosystem, etc.) and possess practical expertise in effectively implementing these advanced systems as a plus
- Proficiency in programming languages such as Python, Rust, or C++ and a track record of working with deep learning frameworks (e.g., pytorch, deepspeed, megatron, vllm, etc.).
- Strong understanding of distributed computing framework & performance tuning and verification for training/finetuning/inference; Being familiar with PEFT, RL, MoE, CoT or Langchain is a plus.
Preferred Qualifications
- Excellent problem-solving skills and a creative mindset to address complex AI challenges. Demonstrated ability to drive research projects from idea to implementation, producing tangible outcomes.
- Published research papers or contributions to the LLM community would be a significant plus.
- Experience with inference tuning and Inference acceleration. Have a deep understanding of GPU and/or other AI accelerators, experience with large scale AI networks, pytorch 2.0 and similar technologies.
- Experience with evaluation of AI systems, LLM application & agent development is desirable.
Job Information
【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $212800 - $450000 annually. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units. Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure). The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
- Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
- Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
- Exercising sound judgment.
About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy – a mission we work towards every day. We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity & Inclusion
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
TikTok Accommodation
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request
See all 566+ OPT Applied Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT Applied Scientist Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as an Applied Scientist
Target companies with active H-1B filing histories
Employers who regularly sponsor Applied Scientists will have a track record of H-1B visa filings. Prioritize companies with dedicated research divisions, such as large tech firms and AI labs, where sponsorship is standard practice rather than an exception.
Lead with your research output, not just your degree
Published papers, patents, and conference presentations signal the kind of specialized expertise that justifies sponsorship. Employers evaluating OPT candidates for Applied Scientist roles weigh research credentials heavily when making the case to their legal team.
Confirm your STEM OPT eligibility before applying
Applied Scientist roles almost always qualify for the 24-month STEM OPT extension, but your degree field must appear on the STEM Designated Degree Program list. Verify this with your DSO before your initial 12-month OPT period ends.
Bring up OPT early in the interview process
Waiting until an offer to mention your work authorization status creates friction. Raise it in the recruiter screen so only genuinely sponsor-willing employers move forward. It saves both sides time and builds trust from the start.
Frame your OPT timeline as a planning asset
Three years of STEM OPT authorization covers two H-1B lottery cycles. Presenting this clearly to hiring managers shows you understand the process and gives cautious employers confidence that there is enough time to get sponsorship right.
Connect with Applied Scientists at the company before applying
Reach out to researchers already in the role you want. They can tell you whether sponsorship is handled internally or through outside counsel, how long the process typically takes at that company, and what the team actually values in candidates.
Applied Scientist OPT: Frequently Asked Questions
Can I work as an Applied Scientist on OPT without employer sponsorship?
Yes. During your OPT authorization period, you can work as an Applied Scientist without any employer petition or sponsorship filing. Your employer does need to report the job to your DSO for STEM OPT compliance, but there is no separate visa petition required. Sponsorship only becomes necessary when your OPT period ends and you need a different work visa to stay authorized.
Do Applied Scientist roles qualify for the STEM OPT extension?
Applied Scientist positions almost universally qualify for the 24-month STEM OPT extension because the role draws directly on STEM degree fields such as computer science, statistics, applied mathematics, and electrical engineering. Your degree must appear on the STEM Designated Degree Program list and the job must be directly related to that field of study. Your DSO can confirm eligibility before you apply for the extension.
How do I find Applied Scientist jobs where employers are open to OPT sponsorship?
Migrate Mate is built specifically for this. The platform surfaces Applied Scientist roles from employers who have a demonstrated history of sponsoring international workers, so you are not wasting applications on companies that will decline once they learn your status. Browsing by role and authorization type there is the most efficient way to identify genuinely sponsor-willing employers in research and tech.
Does a master's degree qualify me for Applied Scientist roles on OPT, or do most employers require a PhD?
Requirements vary by company and team. Large tech companies and AI labs regularly hire Applied Scientists with strong master's degrees, particularly when candidates have published research, internship experience, or a compelling project portfolio. Roles focused on applied product work tend to be more accessible with a master's, while positions in core research or fundamental modeling often prefer a PhD. Read job descriptions carefully and prioritize applying to roles that list a master's as sufficient.
What should I know about the 60-day grace period if my Applied Scientist job ends while I am on OPT?
When employment ends during your OPT period, you enter a 60-day grace period during which you are not authorized to work but remain in valid F-1 status. You can use this window to find a new Applied Scientist role and start work once authorized. If you cannot secure a new position within 60 days, you would need to change status, transfer to a new school, or depart the United States.