핵심 요구사항
- 강화학습(RL) 알고리즘 설계·구현 및 대규모 RL 실험 경험
- 고급 모델 아키텍처(멀티모달, 경량 모델) 전문성
- 시뮬레이션 환경 및 학습 데이터셋 큐레이션 능력
- RL 파이프라인 디버깅·최적화(보상 노이즈, 탐색 전략, 정책 발산 등)
- CS 학위(NLP/ML 박사 우대) 및 A* 학회 논문 실적
- 우수한 영어 커뮤니케이션 능력
Required stack
필요 기술
Reinforcement LearningPyTorchPythonNLPMulti-modal ModelsDeep Learning
Project brief
프로젝트 내용
Headquarters: El Salvador
URL: https://careers.tether.io/
Why Join Us?
Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry.
If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.
Are you ready to be part of the future?
About the job
As a member of the AI model team, you will drive innovation in reinforcement learning approaches for advanced models. Your work will optimize decision-making and adaptive behavior to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges. You will work across a broad spectrum of systems, including resource-efficient models designed for limited hardware environments and complex multi-modal architectures that integrate data such as text, images, and audio.
We expect you to have deep expertise in designing reinforcement learning systems and a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to developing, testing, and implementing novel reinforcement learning algorithms and training frameworks. Your responsibilities include curating specialized simulation environments and training datasets, strengthening baseline policy performance, and identifying as well as resolving bottlenecks in the reinforcement learning process. The ultimate goal is to unlock superior, domain-adapted AI performance and push the limits of what these models can achieve in dynamic, real-world environments.
Responsibilities
-
Develop and implement state-of-the-art reinforcement learning algorithms designed to optimize decision-making processes in both simulated and real-world settings. Establish clear performance targets such as reward maximization and policy stability.
-
Build, run, and monitor controlled reinforcement learning experiments. Track key performance indicators while documenting iterative results and comparing outcomes against established benchmarks.
-
Identify and curate high-quality simulation environments and training datasets that are tailored to specific domain challenges. Set measurable criteria to ensure that the selection and preparation of these resources significantly enhance the learning process and overall model performance.
-
Systematically debug and optimize the reinforcement learning pipeline by analyzing both computational efficiency and learning performance metrics. Address issues such as reward signal noise, exploration strategy, and policy divergence to improve convergence and stability.
-
Collaborate with cross-functional teams to integrate reinforcement learning agents into production systems. Define clear success metrics such as real-world performance improvements and robustness under varied conditions and ensure continuous monitoring and iterative refinements for sustained domain adaptation.
Job requirements
-
A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).
-
Proven experience with large-scale reinforcement learning experiments, including online RL techniques such as Group Relative Policy Optimization (GRPO), is essential. Your contributions should have led to measurable improvements in domain-specific decision-making and overall policy performance.
-
Deep understanding of reinforcement learning algorithms is required, including state-of-the-art online RL methods and other gradient-based optimization approaches like policy gradients, actor-critic, and GRPO. Your expertise should emphasize enhancing policy stability, exploration, and sample efficiency in complex, dynamic environments.
-
Strong expertise in PyTorch and relevant reinforcement learning frameworks is a must. Practical experience in developing RL pipelines, from simulation and online training to post-training evaluation and deploying RL-based solutions in production environments is expected.
-
Demonstrated ability to apply empirical research to overcome reinforcement learning challenges such as sample inefficiency, exploration-exploitation tradeoffs, and training instability. You should be proficient in designing robust evaluation frameworks and iterating on algorithmic innovations to continuously push the boundaries of RL agent performance.
Important information for candidates
Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles:
-
Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/
-
Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website.
-
Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms.
-
Double-check email addresses. All communication from us will come from emails ending in @tether.to or @tether.io
-
We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately.
To apply: https://weworkremotely.com/remote-jobs/tether-ai-research-engineer
URL: https://careers.tether.io/
Why Join Us?
Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry.
If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.
Are you ready to be part of the future?
About the job
As a member of the AI model team, you will drive innovation in reinforcement learning approaches for advanced models. Your work will optimize decision-making and adaptive behavior to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges. You will work across a broad spectrum of systems, including resource-efficient models designed for limited hardware environments and complex multi-modal architectures that integrate data such as text, images, and audio.
We expect you to have deep expertise in designing reinforcement learning systems and a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to developing, testing, and implementing novel reinforcement learning algorithms and training frameworks. Your responsibilities include curating specialized simulation environments and training datasets, strengthening baseline policy performance, and identifying as well as resolving bottlenecks in the reinforcement learning process. The ultimate goal is to unlock superior, domain-adapted AI performance and push the limits of what these models can achieve in dynamic, real-world environments.
Responsibilities
-
Develop and implement state-of-the-art reinforcement learning algorithms designed to optimize decision-making processes in both simulated and real-world settings. Establish clear performance targets such as reward maximization and policy stability.
-
Build, run, and monitor controlled reinforcement learning experiments. Track key performance indicators while documenting iterative results and comparing outcomes against established benchmarks.
-
Identify and curate high-quality simulation environments and training datasets that are tailored to specific domain challenges. Set measurable criteria to ensure that the selection and preparation of these resources significantly enhance the learning process and overall model performance.
-
Systematically debug and optimize the reinforcement learning pipeline by analyzing both computational efficiency and learning performance metrics. Address issues such as reward signal noise, exploration strategy, and policy divergence to improve convergence and stability.
-
Collaborate with cross-functional teams to integrate reinforcement learning agents into production systems. Define clear success metrics such as real-world performance improvements and robustness under varied conditions and ensure continuous monitoring and iterative refinements for sustained domain adaptation.
Job requirements
-
A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).
-
Proven experience with large-scale reinforcement learning experiments, including online RL techniques such as Group Relative Policy Optimization (GRPO), is essential. Your contributions should have led to measurable improvements in domain-specific decision-making and overall policy performance.
-
Deep understanding of reinforcement learning algorithms is required, including state-of-the-art online RL methods and other gradient-based optimization approaches like policy gradients, actor-critic, and GRPO. Your expertise should emphasize enhancing policy stability, exploration, and sample efficiency in complex, dynamic environments.
-
Strong expertise in PyTorch and relevant reinforcement learning frameworks is a must. Practical experience in developing RL pipelines, from simulation and online training to post-training evaluation and deploying RL-based solutions in production environments is expected.
-
Demonstrated ability to apply empirical research to overcome reinforcement learning challenges such as sample inefficiency, exploration-exploitation tradeoffs, and training instability. You should be proficient in designing robust evaluation frameworks and iterating on algorithmic innovations to continuously push the boundaries of RL agent performance.
Important information for candidates
Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles:
-
Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/
-
Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website.
-
Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms.
-
Double-check email addresses. All communication from us will come from emails ending in @tether.to or @tether.io
-
We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately.
To apply: https://weworkremotely.com/remote-jobs/tether-ai-research-engineer
지원 기회 분석
지원 전에 볼 것
예상 산출물
- 신규 강화학습 알고리즘 및 학습 프레임워크
- 큐레이션된 시뮬레이션 환경 및 학습 데이터셋
- RL 실험 결과 문서 및 벤치마크 비교 리포트
- 프로덕션 시스템에 통합된 RL 에이전트
매력 포인트
- 글로벌 완전 원격 근무
- 대형 핀테크(Tether)의 최첨단 AI R&D 참여
- 박사급 연구 역량을 발휘할 수 있는 고난도 포지션
확인할 점
- 예산·기간·구체적 처우가 명시되지 않음
- 카테고리가 'Back-End Programming'으로 표기돼 실제 AI 연구직과 불일치
Client signal
클라이언트 정보
플랫폼에서 상세 정보를 확인하세요.