ML Research Engineer Jobs

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Lead the LLM team, owning the architecture, training, and deployment of models powering the core product. Act as a player/coach by architecting the system, mentoring the team, and actively writing production code primarily in Python/PyTorch. Optimize the core chat loop focusing on context windows, memory/RAG retrieval, and inference latency to deliver a seamless real-time user experience. Drive the strategy for model lifecycle management including supervised fine-tuning (SFT), reinforcement learning with human feedback (RLHF), and direct preference optimization (DPO), deciding when to prompt, fine-tune, or architect new retrieval-augmented generation pipelines. Manage the data engine involving sourcing, labeling, and cleaning datasets to enhance model steerability and multicultural performance. Architect and build high-precision moderation systems by designing and training custom classifiers to detect and filter non-consensual or illegal content in an explicit environment, moving beyond binary safe/unsafe flags towards nuanced, context-aware moderation.

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The Tech Lead will act as a player/coach, architecting the system and mentoring the team while spending significant time hands-on in the codebase (Python/PyTorch). They will own the core chat loop, optimizing context windows, memory/RAG retrieval, and inference latency to ensure a seamless, real-time experience. They will drive the strategy for supervised fine-tuning (SFT) and reinforcement learning with human feedback/preference optimization (RLHF/DPO), deciding when to prompt, fine-tune, or architect a new RAG pipeline. They will manage the data engine overseeing the sourcing, labeling, and cleaning of diverse datasets to improve model steerability and multicultural performance. Additionally, they will architect high-precision moderation by designing and training custom classifiers to detect and filter non-consensual or illegal content within an explicit environment and create nuanced, context-aware moderation systems beyond binary safe/unsafe flags.

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The Tech Lead will take the helm of the LLM team, owning the architecture, training, and deployment of the models powering the core product. Responsibilities include writing production code, defining alignment strategies, and shipping features used by millions globally. They will act as a player/coach, architecting the system and mentoring the team while spending significant time hands-on in Python/PyTorch code. They will own the core chat loop by optimizing context windows, memory/RAG retrieval, and inference latency to ensure a seamless real-time experience. They will drive the strategy for Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback/Direct Preference Optimization (RLHF/DPO), managing when to prompt, fine-tune, or architect new RAG pipelines. Additionally, they will oversee the sourcing, labeling, and cleaning of diverse datasets to improve model steerability and multicultural performance. Another key responsibility is to architect high-precision moderation by designing and training custom classifiers to detect and filter non-consensual or illegal content in an explicit environment, moving beyond binary safe/unsafe flags to nuanced, context-aware moderation systems.

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As Tech Lead of the LLM team, you will architect the system and mentor your team while spending significant time hands-on coding in Python/PyTorch. You will own the core chat loop, optimizing context windows, memory/RAG retrieval, and inference latency to enable a real-time user experience. You will drive the strategy for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF/DPO), deciding when to prompt, fine-tune, or build new RAG pipelines. You will manage the data engine by overseeing sourcing, labeling, and cleaning diverse datasets to enhance model steerability and multicultural performance. Additionally, you will design and train custom classifiers for nuanced, context-aware moderation to detect and filter non-consensual or illegal content in an explicit environment, moving beyond simple binary safety flags to build a precise moderation system.

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Lead the LLM team, owning the architecture, training, and deployment of models powering the core product. Act as a player/coach by architecting the system, mentoring the team, and spending significant time hands-on in the codebase using Python/PyTorch. Own and optimize the core chat loop, including context windows, memory/RAG retrieval, and inference latency to provide a seamless, real-time experience. Drive strategy for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF/DPO), including decisions on prompting, fine-tuning, and designing new RAG pipelines. Manage the data engine by overseeing sourcing, labeling, and cleaning datasets to improve model steerability and multicultural performance. Architect and train custom classifiers to detect and filter non-consensual or illegal content in an explicit environment, developing nuanced, context-aware moderation systems beyond simple safe/unsafe flags.

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The Tech Lead will ship code and lead from the front by acting as a player/coach, architecting the system, mentoring the team, and spending significant time hands-on in the codebase using Python and PyTorch. They will own the core chat loop, optimizing context windows, memory/RAG retrieval, and inference latency to ensure a seamless real-time experience. They will also own the model lifecycle by driving the strategy for supervised fine-tuning (SFT) and RLHF/DPO preference optimization, deciding when to prompt, fine-tune, or architect a new retrieval-augmented generation (RAG) pipeline. Additionally, they will manage the data engine, overseeing sourcing, labeling, and cleaning diverse datasets to improve model steerability and multicultural performance. Another responsibility is to architect high-precision moderation by designing and training custom classifiers to detect and filter non-consensual or illegal content within an explicit environment, moving beyond binary safe/unsafe flags to create nuanced, context-aware moderation systems.

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As Tech Lead, you will ship code and lead from the front, acting as a player/coach by architecting the system, mentoring the team, and spending significant time hands-on coding in Python/PyTorch. You will own the core chat loop, optimizing context windows, memory/RAG retrieval, and inference latency to ensure a real-time seamless experience. You will own the model lifecycle by driving strategies for supervised fine-tuning (SFT), RLHF/DPO, deciding prompt timing, fine-tuning, and RAG pipeline architecture. You will manage the data engine overseeing sourcing, labeling, and cleaning diverse datasets to improve model steerability and multicultural performance. Additionally, you will architect high-precision moderation systems by designing and training custom classifiers to detect and filter non-consensual or illegal content in an explicit environment and develop nuanced, context-aware moderation systems beyond simple safe/unsafe flags.

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Lead the LLM team by architecting, training, and deploying models that power the core product. Act as a player/coach by spending significant time hands-on in the codebase with Python/PyTorch while mentoring the team. Own and optimize the core chat loop by managing context windows, memory/RAG retrieval, and inference latency to ensure a seamless, real-time experience. Drive the strategy for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)/DPO preference optimization, deciding when to prompt, fine-tune, or architect new RAG pipelines. Manage the sourcing, labeling, and cleaning of diverse datasets to improve model steerability and multicultural performance. Design and train custom classifiers for high-precision moderation to detect and filter non-consensual or illegal content within an explicit environment, moving beyond binary safe/unsafe flags to create nuanced, context-aware moderation systems.

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Lead the LLM team of 3 engineers by architecting the system, mentoring the team, and actively writing production code primarily in Python and PyTorch. Own the core chat loop by optimizing context windows, memory/RAG retrieval, and inference latency to ensure a seamless, real-time experience for users. Drive the strategy for Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback / Direct Preference Optimization (RLHF/DPO), deciding when to prompt, fine-tune, or architect new retrieval augmented generation (RAG) pipelines. Manage data sourcing, labeling, and cleaning to enhance model steerability and performance across cultures. Architect and train custom moderation classifiers to detect and filter non-consensual or illegal content within the explicit environment, creating nuanced, context-aware moderation systems beyond simple safe/unsafe filtering.

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Lead the LLM team (currently 3 engineers) and own the architecture, training, and deployment of models that power EverAI's core product. Act as a player/coach by architecting the system, mentoring the team, and spending significant time hands-on in the codebase using Python/PyTorch. Own the core chat loop by optimizing context windows, memory/RAG retrieval, and inference latency to ensure a seamless, real-time experience. Drive the strategy for Supervised Fine-Tuning (SFT) and RLHF/DPO (Preference Optimization), deciding when to prompt, fine-tune, or architect new RAG pipelines. Manage the data engine by overseeing the sourcing, labeling, and cleaning of diverse datasets to improve model steerability and multicultural performance. Architect high-precision moderation systems by designing and training custom classifiers to detect and filter non-consensual or illegal content in an explicit environment, moving beyond binary safe/unsafe flags to nuanced context-aware moderation systems.