6 Ai Safety Jobs

As a Principal Research Scientist focusing on AI Alignment at Ola Krutrim in Bangalore, India, you will lead the efforts in Trust and Safety, Interpretability, and Red Teaming within the AI division. Your role will be crucial in ensuring that the AI systems developed are safe, ethical, interpretable, and reliable, with a significant impact on millions of lives. You will be at the forefront of cutting-edge AI research, guiding the implementation of technologies that adhere to the highest standards of safety and transparency. Your responsibilities will include providing strategic leadership for the AI Alignment division, overseeing teams dedicated to Trust and Safety, Interpretability, and Red Teaming. You will work closely with the Lead AI Trust and Safety Research Scientist and Lead AI Interpretability Research Scientist to align goals and methodologies. Developing comprehensive strategies for AI alignment, integrating advanced safety and interpretability techniques, and establishing best practices for red teaming exercises to identify vulnerabilities will be key aspects of your role. Moreover, you will collaborate with product and research teams to implement safety and interpretability aspects throughout the AI development lifecycle. Staying updated on AI ethics, safety, and interpretability research, representing the company in industry events, and managing resource allocation and strategic planning for the AI Alignment division are also part of your responsibilities. Mentoring and developing team members, fostering innovation, and communicating progress and recommendations to executive leadership will be essential in this role. To qualify for this position, you should hold a Ph.D. in Computer Science, Machine Learning, or a related field with a focus on AI safety, ethics, and interpretability. With at least 7 years of experience in AI research and development, including 3 years in a leadership role, you should have expertise in AI safety, interpretability, and red teaming methodologies. Strong knowledge of advanced techniques such as Reinforcement Learning, Proximal Policy Optimization, and attention-based methods, along with experience in overseeing red teaming exercises for AI systems, are required. Your visionary mindset, along with excellent communication skills, project management abilities, and a proven track record in AI safety, ethics, and interpretability research, will be instrumental in shaping the future of responsible AI development at Ola Krutrim. By leading cross-functional initiatives and fostering a culture of continuous learning and innovation, you will contribute to building public trust in AI technologies and positioning the company as a leader in ethical and responsible AI development.,

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.,

As an AI Research Engineer at Mantra Softech, a technology leader in biometric solutions venturing into AI-powered smart home devices, you will play a crucial role in shaping the future of connected living. Your primary responsibility will be to lead initiatives in LLMs, Edge AI, sensor fusion, ML Models, and natural language interfaces to develop AI-powered experiences across innovative smart home devices. You will be instrumental in designing and developing GenAI-driven AI agents capable of interpreting multimodal inputs and executing context-aware actions. Your role will involve driving initiatives in agentic AI frameworks, architecting LLMOps workflows, and deploying real-time AI applications across cloud and edge environments with a focus on performance and reliability. To excel in this role, you must have a proven track record of designing, deploying, and delivering AI/ML systems into production, with expertise in LLMs, GenAI pipelines, prompt engineering, and agentic AI. Proficiency in Python (and/or C/C++), PyTorch/TensorFlow, and AI/ML frameworks is essential, along with experience in LLMOps, time-series analysis, model optimization, and SQL. Preferred skills for this role include familiarity with LangChain, LlamaIndex, digital twins, telemetry, AI safety, cloud deployment, and microservices. A master's degree in AI/ML, Computer Science, or a related field from a reputed institute, coupled with 4-6 years of industry experience in GenAI and production deployments, is required. If you are passionate about GenAI applications, AI agents, and transforming natural language prompts into actionable insights, this role offers you the opportunity to work on cutting-edge AI projects that will drive the future of connected living. Join us at Mantra Softech and be part of a world-class AI team that is revolutionizing the way we interact with smart home devices.,

You are a highly skilled and innovative Senior Python Developer specializing in Generative AI and MLOps tools such as Argo Workflows and Kubernetes. Your role involves designing and deploying scalable GenAI applications, constructing robust pipelines, and integrating AI models into production environments. As a Python Developer, you will be responsible for designing, developing, and fine-tuning generative models like GPT, LLAMA, Mistral, FLAN, and T5. You will also build and optimize Retrieval-Augmented Generation (RAG) pipelines, implement model evaluation frameworks, and ensure responsible AI deployment. Your expertise in writing efficient, reusable, and testable Python code for AI/ML applications will be crucial in collaborating with data scientists to integrate models into scalable systems and maintaining high-performance Python applications for real-time inference. In the realm of MLOps and Infrastructure, you will develop and manage workflows using Argo Workflows for model training and deployment. Additionally, you will containerize applications using Docker, orchestrate deployments with Kubernetes, and monitor and optimize GPU usage on cloud platforms like AWS, Azure, and Databricks. Collaboration and Delivery are key aspects of your role, as you will work cross-functionally with engineering, product, and data teams to deliver AI solutions. You will interpret research papers and model architecture diagrams to guide implementation, ensuring timely delivery of AI projects with high reliability and scalability. To excel in this role, you should hold a Bachelors or Masters degree in Computer Science, AI/ML, or a related field, with at least 4+ years of experience in Python development and data science. Hands-on experience with LLM APIs, Hugging Face, transformer models, and proficiency in deep learning frameworks like PyTorch, TensorFlow, and Keras are essential. A strong understanding of Argo Workflows, Kubernetes, containerization, cloud platforms, GPU optimization, NLP tools, SQL, and Spark will be advantageous. Preferred experience includes fine-tuning models using LoRA, QLoRA, and quantization techniques, building multi-agent systems, multimodal applications, and knowledge of AI safety, ethics, and compliance. Join Tredence, a leading analytics partner dedicated to transforming data into actionable insights for Fortune 500 clients. With a global presence and a mission to drive unparalleled business value through advanced AI and data science, we welcome you to embark on this innovative journey with us.,

As a Senior Scientist specializing in Responsible AI & Research Integration, you will be playing a critical role in bridging the gap between academic research in AI safety and the practical development of AI products. Based in Bangalore, this high-impact position requires 10 to 18 years of experience in the field. Your primary responsibility will be to advance the frontiers of Responsible AI and AI safety through both foundational and applied research. Approximately 60% of your time will be dedicated to conducting research on topics such as model alignment, transparency, behavioral safety, and oversight mechanisms for autonomous systems. The remaining 40% will involve translating these research insights into practical tools, features, and governance components that can be integrated into internal systems and external offerings. Collaboration will be key in this role, as you will work closely with the AI Research Lab and Responsible AI Office to define research agendas and translate findings into product features and governance frameworks. Additionally, you will be involved in building partnerships with academic labs, participating in external working groups, and providing strategic intelligence on the evolving ecosystem of responsible AI technologies and companies. Your responsibilities will also include developing product roadmap specifications, evaluating early-stage startups in the AI safety space, and monitoring the competitive landscape to identify market gaps in responsible AI tooling. Building collaborative relationships with academic labs, research consortia, and external fellows, as well as representing the company in research summits and public forums, will be part of your external engagement activities. To excel in this role, you should have a PhD in Computer Science, Artificial Intelligence, or a related discipline, with a strong publication record in AI safety research. Experience in translating research into production-ready tools, collaborating with interdisciplinary teams, and evaluating early-stage AI companies will be essential. Strong communication skills, the ability to synthesize insights from academic research, and a network within the responsible AI research community will also be valuable assets. If you are passionate about driving advancements in Responsible AI, thriving at the intersection of science, systems thinking, and strategic influence, and have a track record of contributing to cutting-edge research and product development, this role offers a unique opportunity to make a significant impact in the field.,

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.,