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Research Engineer — ML RL Agents
#удаленка
Company: Affine.io
Salary: $150k - $500k
☑️Responsibilities
-Design decentralized RL systems that incentivize miners to train, refine, and host high-quality agentic LLMs on the Bittensor subnet.
-Develop evaluation frameworks to assess model performance, safety, and alignment—including task design, metrics, adversarial testing, and red-teaming.
-Advance RL for agentic models by researching and applying cutting-edge RL and alignment techniques to improve the training–evaluation loop.
-Prototype and scale algorithms: explore new agent architectures and post-training methods, then build reproducible pipelines for finetuning, evaluation, and data flow.
-Contribute to live competitive benchmarks, deploying new approaches in production and ensuring the system rewards genuine intelligence gains rather than gaming.

☑️Requirements
-Reinforcement Learning expertise with deep knowledge and hands-on experience in RL algorithms, design, and tuning. Background in multi-agent systems, mechanism design, or RLHF is a strong plus.
-Strong engineering skills in Python and experience building production-level ML systems with PyTorch, JAX, or TensorFlow.
-Distributed systems experience, with comfort designing and scaling high-performance, reliable infrastructure.
-Knowledge of LLMs and tool use, including how models interact with APIs, external tools, and function calling.
-Advanced academic or practical background: Master’s or PhD in a relevant field, or equivalent applied research and engineering experience.
Contact: https://applicantai.com/affine-io/research-engineer-ml-rl-agents/10570

🔥 Подписаться на наши каналы / @best_itjob / @it_rab

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www.tgoop.com/job_python/6725

2.75K viewsSep 17 at 05:00

Research Engineer — ML RL Agents
#удаленка
Company: Affine.io
Salary: $150k - $500k
☑️Responsibilities
-Design decentralized RL systems that incentivize miners to train, refine, and host high-quality agentic LLMs on the Bittensor subnet.
-Develop evaluation frameworks to assess model performance, safety, and alignment—including task design, metrics, adversarial testing, and red-teaming.
-Advance RL for agentic models by researching and applying cutting-edge RL and alignment techniques to improve the training–evaluation loop.
-Prototype and scale algorithms: explore new agent architectures and post-training methods, then build reproducible pipelines for finetuning, evaluation, and data flow.
-Contribute to live competitive benchmarks, deploying new approaches in production and ensuring the system rewards genuine intelligence gains rather than gaming.

☑️Requirements
-Reinforcement Learning expertise with deep knowledge and hands-on experience in RL algorithms, design, and tuning. Background in multi-agent systems, mechanism design, or RLHF is a strong plus.
-Strong engineering skills in Python and experience building production-level ML systems with PyTorch, JAX, or TensorFlow.
-Distributed systems experience, with comfort designing and scaling high-performance, reliable infrastructure.
-Knowledge of LLMs and tool use, including how models interact with APIs, external tools, and function calling.
-Advanced academic or practical background: Master’s or PhD in a relevant field, or equivalent applied research and engineering experience.
Contact: https://applicantai.com/affine-io/research-engineer-ml-rl-agents/10570

🔥 Подписаться на наши каналы / @best_itjob / @it_rab

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