AI Research Engineer (Agentic Reinforcement Learning)

Jobgether; Oregon; Hybrid; USD 150,000 - 220,000; Full time

Benefits offered by this job

Fully remote

International collaboration

Cutting-edge AI research

Conferences & recognition

Career growth

Global tech environment

Job summary

Jobgether is seeking an AI Research Engineer (Agentic Reinforcement Learning) based in the United States. You will drive research on agentic RL for multimodal systems, spanning text, images, audio, and more.

You will design scalable training infrastructure, optimize rewards, and advance diffusion-based generative and autoregressive models across modalities. The role requires a strong publication record, PhD or MS in ML/CS, and hands-on experience with large-scale RL experiments.

Qualifications

• Master's degree in CS or a related field; PhD preferred.
• Strong track record of AI research with publications at top conferences.
• Experience conducting large-scale reinforcement learning experiments in multimodal/vision-centric systems.
• Proven ability to improve policy performance in complex environments.
• Deep understanding of RL algorithms, optimization, and stability in high‑dimensional settings.

Responsibilities

• Research and develop RL algorithms for multimodal models, including diffusion-based approaches for image generation and autoregressive multimodal understanding.
• Design scalable RL infrastructure supporting distributed training for complex multimodal systems.
• Develop and optimize reward modeling to improve training stability and prevent reward hacking.
• Create and maintain multimodal simulation environments and training datasets for evaluation and benchmarking.
• Design rigorous evaluation protocols to measure progress and compare against baselines.
• Analyze policy performance across modalities to identify bottlenecks and improve cross-modal alignment.
• Investigate emerging RL paradigms to enhance learning efficiency and generalization.
• Publish and present research findings at ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV.

Skills

Reinforcement learning

Multimodal AI

PyTorch

Experimentation

Diffusion models

Policy optimization

Online RL

Research publications

Education

Master's degree in Computer Science or related field

PhD in ML/NLP/CV or related (preferred)

Tools

PyTorch

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Research Engineer (Agentic Reinforcement Learning) based in United States.

As an AI Research Engineer specializing in Agentic Reinforcement Learning, you will drive research and innovation in next-generation intelligent systems.

You will develop advanced reinforcement learning techniques that improve decision‑making, adaptive behavior, and performance across multimodal AI architectures.

Working with text, images, audio, and other data modalities, you will help build models capable of solving complex, real‑world challenges.

Your work will involve designing scalable training infrastructure, optimizing reward mechanisms, and improving the reliability of autonomous AI agents.

You will explore cutting‑edge approaches involving diffusion‑based generative models, autoregressive architectures, and unified multimodal frameworks.

Through rigorous experimentation and benchmarking, you will push the boundaries of model performance, efficiency, and generalization.

This fully remote opportunity offers the chance to contribute to impactful AI research within a fast‑moving, globally distributed technology environment.

Accountabilities

• Research and develop reinforcement learning algorithms for multimodal models, including diffusion‑based approaches for image generation, autoregressive models for multimodal understanding, and unified architectures integrating multiple modalities.
• Design and implement scalable reinforcement learning infrastructure supporting distributed training across complex multimodal systems.
• Develop and optimize reward modeling strategies to improve training stability, align model behavior with desired outcomes, and prevent reward hacking and related failure modes.
• Create, curate, and maintain multimodal simulation environments and training datasets to support robust model development, evaluation, and benchmarking.
• Design and implement rigorous evaluation protocols to measure model performance, monitor progress against established baselines, and validate research improvements.
• Analyze and optimize policy performance across different modalities by identifying bottlenecks in training, credit assignment, reward optimization, and cross‑modal alignment.
• Investigate emerging reinforcement learning paradigms that enable AI agents to learn more effectively from environmental feedback.
• Improve model exploration, decision‑making, and adaptive behavior to achieve state‑of‑the‑art performance in dynamic, real‑world environments.
• Conduct large‑scale experiments and translate empirical findings into practical algorithmic improvements.
• Publish and present research findings at leading international conferences, including ICML, NeurIPS, ICLR, CVPR, ICCV, and ECCV.

Requirements

The ideal candidate combines strong theoretical knowledge of reinforcement learning with hands‑on experience developing and optimizing large‑scale multimodal AI systems. The following qualifications and technical skills are expected:

• Master's degree in Computer Science or a related discipline. A PhD in Machine Learning, Natural Language Processing, Computer Vision, or a closely related field is preferred.
• Strong track record of AI research, including publications in leading international conferences.
• Demonstrated experience conducting large‑scale reinforcement learning experiments in multimodal and vision‑centric systems, including online reinforcement learning environments.
• Proven ability to improve domain‑specific decision‑making and achieve measurable gains in policy performance.
• Deep understanding of reinforcement learning algorithms and optimization techniques applied to vision and multimodal learning.
• Strong knowledge of policy stability, exploration strategies, sample efficiency, and optimization in complex, high‑dimensional environments involving images, video, and other modalities.
• Advanced proficiency in PyTorch and modern deep learning frameworks for computer vision and multimodal AI.
• Hands‑on experience building end‑to‑end reinforcement learning pipelines covering simulation, model training, evaluation, and production deployment.
• Ability to address fundamental reinforcement learning challenges, including sample inefficiency, exploration‑exploitation trade‑offs, and training instability.
• Experience designing robust evaluation frameworks and using empirical research to continuously improve algorithmic performance.
• Established research publication record at top‑tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, or ECCV.
• Strong analytical thinking, scientific problem‑solving skills, and the ability to translate advanced research into practical AI solutions.

Benefits

Fully Remote Work:

Work remotely from anywhere in the world, with a globally distributed team.

International Collaboration:

Collaborate with talented researchers, engineers, and technology professionals across different countries and disciplines.

Cutting‑Edge AI Research:

Work on advanced reinforcement learning, multimodal architectures, generative AI, and next‑generation intelligent systems.

Research and Innovation:

Explore new methodologies and contribute to state‑of‑the‑art developments in artificial intelligence.

Scientific Recognition:

Opportunities to publish research and present findings at leading international AI and machine learning conferences.

Professional Growth:

Develop expertise in emerging AI technologies while contributing to challenging, high‑impact research initiatives.

Global Technology Environment:

Be part of an ambitious, fast‑growing organization working across AI, digital finance, peer‑to‑peer technology, and other innovative fields.