4 Nccl Jobs

As a member of our team, your primary responsibility will be the development and training of foundational models across various modalities. You will be involved in the end-to-end lifecycle management of foundational model development, starting from data curation to model deployment, by collaborating closely with core team members. Your role will also entail conducting research to enhance model accuracy and efficiency, as well as implementing state-of-the-art AI techniques in Text/Speech and language processing. Collaboration with cross-functional teams will be essential as you work towards building robust AI stacks and seamlessly integrating them into production pipelines. You will be expected to develop pipelines for debugging, CI/CD, and ensuring observability throughout the development process. Demonstrating your ability to lead projects and offer innovative solutions will be crucial, along with documenting technical processes, model architectures, and experimental results, while maintaining clear and organized code repositories. Ideally, you should hold a Bachelor's or Master's degree in a related field and possess 2 to 5 years of industry experience in applied AI/ML. Proficiency in Python programming is a must, along with familiarity with a selection of tools such as TensorFlow, PyTorch, HF Transformers, NeMo, SLURM, Ray, Pytorch DDP, Deepspeed, NCCL, Git, DVC, MLFlow, W&B, KubeFlow, Dask, Milvus, Apache Spark, Numpy, Whisper, Voicebox, VALL-E, HuBERT/Unitspeech, LLMOPs Tools, Dockers, DSPy, Langgraph, langchain, and llamaindex. If you are passionate about AI and eager to contribute to cutting-edge projects in the field, we welcome your application to join our dynamic team.,

The job involves designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. Building simulation frameworks grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Developing systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning. Researching fractal intelligence structures, swarm-based agent coordination, and autopoietic systems. Advancing Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Integrating logic into distributed decision graphs aligned with business and ethical constraints. Publishing cutting-edge results and mentoring contributors in reflective system design and emergent AI theory. Building scalable simulations of multi-agent ecosystems within the Mobius runtime. You should have a Ph.D. or M.Tech in Artificial Intelligence, Cognitive Science, Complex Systems, Applied Mathematics, or equivalent experience. Proven expertise in meta-learning, recursive architectures, and AI safety. Strong knowledge of distributed systems, multi-agent environments, and decentralized coordination. Proficiency in formal and theoretical foundations like Bayesian modeling, graph theory, and logical inference. Strong implementation skills in Python, additional proficiency in C++, functional or symbolic languages are a plus. A publication record in areas intersecting AI research, complexity science, and/or emergent systems is required. Preferred qualifications include experience with neurosymbolic architectures, hybrid AI systems, fractal modeling, attractor theory, complex adaptive dynamics, topos theory, category theory, logic-based semantics, knowledge ontologies, OWL/RDF, semantic reasoners, autopoiesis, teleo-dynamics, biologically inspired system design, swarm intelligence, self-organizing behavior, emergent coordination, distributed learning systems like Ray, Spark, MPI, or agent-based simulators. Technical proficiency required in Python, preferred in C++, Haskell, Lisp, or Prolog for symbolic reasoning. Familiarity with frameworks like PyTorch, TensorFlow, distributed systems like Ray, Apache Spark, Dask, Kubernetes, knowledge technologies including Neo4j, RDF, OWL, SPARQL, experiment management tools such as MLflow, Weights & Biases, GPU and HPC systems like CUDA, NCCL, Slurm, and formal modeling tools like Z3, TLA+, Coq, Isabelle. Core research domains include recursive self-improvement and introspective AI, graph theory, graph rewriting, knowledge graphs, neurosymbolic systems, ontological reasoning, fractal intelligence, dynamic attractor-based learning, Bayesian reasoning, cognitive dynamics, swarm intelligence, decentralized consensus modeling, topos theory, autopoietic system architectures, teleo-dynamics, and goal-driven adaptation in complex systems.,

We are looking for a skilled and enthusiastic Applied AI/ML Engineer to be a part of our team. As an Applied AI/ML Engineer, you will be responsible for leading the entire process of foundational model development, focusing on cutting-edge generative AI techniques. Your main objective will be to implement efficient learning methods for data and compute, specifically addressing challenges relevant to the Indian scenario. Your tasks will involve optimizing model training and inference pipelines, deploying production-ready models, ensuring scalability through distributed systems, and fine-tuning models for domain adaptation. Collaboration with various teams will be essential as you work towards building strong AI stacks and seamlessly integrating them into production pipelines. Apart from conducting research and experiments, you will be crucial in converting advanced models into operational systems that generate tangible results. Your leadership in this field will involve working closely with technical team members and subject matter experts, documenting technical processes, and maintaining well-structured codebases to encourage innovation and reproducibility. This position is perfect for proactive individuals who are passionate about spearheading significant advancements in generative AI and implementing scalable solutions for real-world impact. Your responsibilities will include: - Developing and training foundational models across different modalities - Managing the end-to-end lifecycle of foundational model development, from data curation to model deployment, through collaboration with core team members - Conducting research to enhance model accuracy and efficiency - Applying state-of-the-art AI techniques in Text/Speech and language processing - Collaborating with cross-functional teams to construct robust AI stacks and smoothly integrate them into production pipelines - Creating pipelines for debugging, CI/CD, and observability of the development process - Demonstrating project leadership and offering innovative solutions - Documenting technical processes, model architectures, and experimental outcomes, while maintaining clear and organized code repositories To be eligible for this role, you should hold a Bachelor's or Master's degree in a related field and possess 2 to 5 years of industry experience in applied AI/ML. Minimum requirements for this position include proficiency in Python programming and familiarity with 3-4 tools from the specified list below: - Foundational model libraries and frameworks (TensorFlow, PyTorch, HF Transformers, NeMo, etc) - Experience with distributed training (SLURM, Ray, Pytorch DDP, Deepspeed, NCCL, etc) - Inference servers (vLLM) - Version control systems and observability (Git, DVC, MLFlow, W&B, KubeFlow) - Data analysis and curation tools (Dask, Milvus, Apache Spark, Numpy) - Text-to-Speech tools (Whisper, Voicebox, VALL-E (X), HuBERT/Unitspeech) - LLMOPs Tools, Dockers, etc - Hands-on experience with AI application libraries and frameworks (DSPy, Langgraph, langchain, llamaindex, etc),

You will be responsible for designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. Your role will involve building simulation frameworks for behavior grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Additionally, you will develop systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning. Conducting research on fractal intelligence structures, swarm-based agent coordination, and autopoietic systems will be part of your responsibilities. You are expected to advance Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Integration of logic into distributed, policy-scoped decision graphs aligned with business and ethical constraints is crucial. Furthermore, publishing cutting-edge results and mentoring contributors in reflective system design and emergent AI theory will be part of your duties. Lastly, building scalable simulations of multi-agent, goal-directed, and adaptive ecosystems within the Mobius runtime is an essential aspect of the role. In terms of qualifications, you should have proven expertise in meta-learning, recursive architectures, and AI safety. Proficiency in distributed systems, multi-agent environments, and decentralized coordination is necessary. Strong implementation skills in Python are required, with additional proficiency in C++, functional, or symbolic languages being a plus. A publication record in areas intersecting AI research, complexity science, and/or emergent systems is also desired. Preferred qualifications include experience with neurosymbolic architectures and hybrid AI systems, fractal modeling, attractor theory, complex adaptive dynamics, topos theory, category theory, logic-based semantics, knowledge ontologies, OWL/RDF, semantic reasoners, autopoiesis, teleo-dynamics, biologically inspired system design, swarm intelligence, self-organizing behavior, emergent coordination, and distributed learning systems. In terms of technical proficiency, you should be proficient in programming languages such as Python (required), C++, Haskell, Lisp, or Prolog (preferred for symbolic reasoning), frameworks like PyTorch and TensorFlow, distributed systems including Ray, Apache Spark, Dask, Kubernetes, knowledge technologies like Neo4j, RDF, OWL, SPARQL, experiment management tools like MLflow, Weights & Biases, and GPU and HPC systems like CUDA, NCCL, Slurm. Familiarity with formal modeling tools like Z3, TLA+, Coq, Isabelle is also beneficial. Your core research domains will include recursive self-improvement and introspective AI, graph theory, graph rewriting, and knowledge graphs, neurosymbolic systems and ontological reasoning, fractal intelligence and dynamic attractor-based learning, Bayesian reasoning under uncertainty and cognitive dynamics, swarm intelligence and decentralized consensus modeling, top os theory, and the abstract structure of logic spaces, autopoietic, self-sustaining system architectures, and teleo-dynamics and goal-driven adaptation in complex systems.,