AI Research Engineer (Agentic Reinforcement Learning)

Jobgether; Australia; Remote; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Fully remote work from anywhere

Globally distributed team

Opportunities to publish at major AI/0

Job summary

Jobgether seeks an AI Research Engineer (Agentic Reinforcement Learning) based in Australia. The role focuses on advancing reinforcement learning for multimodal AI systems, including diffusion-based and autoregressive models, with scalable training infrastructure and rigorous evaluation.

The ideal candidate has a strong publication record and hands-on experience building end-to-end RL pipelines. This fully remote opportunity involves working in a globally distributed team and contributing to

Qualifications

• Master's degree in CS or related field; PhD preferred.
• Strong AI research track record with publications at top conferences.
• Experience in large-scale RL experiments in multimodal/vision systems.
• Proficiency in PyTorch and modern deep learning frameworks.

Responsibilities

• Conduct advanced research and develop scalable reinforcement learning systems for multimodal AI applications.
• Design scalable reinforcement learning infrastructure for distributed training across complex multimodal systems.
• Develop and optimize reward modeling strategies to improve training stability and alignment with goals.
• Create, curate, and maintain multimodal simulation environments and training datasets.

Skills

Reinforcement learning

Multimodal learning

Academic publications

Experiment design

Research communication

Education

Master's degree in Computer Science or related field

PhD in ML / NLP / CV preferred

Tools

PyTorch

Deep learning frameworks

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 Australia.

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

In this role, you will conduct advanced research and develop scalable reinforcement learning systems for multimodal AI applications. Your main responsibilities will include:

• 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.