{"id":744357,"url":"https://alion.io/job/virallens-ai-researcher","title":"AI Researcher","company":{"id":689480,"name":"Virallens","domain":"virallens.com","url":"https://alion.io/company/virallens","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Computer Vision","optional":false},{"name":"Embodied AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Interpretability","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-08-21T00:00:00Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-11-01T07:06:42Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"description":"Role : AI Researcher\nLocation: Bengaluru, Karnataka - On-site\nEmployment Type: Full-time\nAbout the Role\nAs an AI Researcher, you will research, design, experiment with, and develop advanced Artificial Intelligence and Machine Learning systems that power our AI products and enterprise applications.\nThis is a broad AI research role, not limited to NLP or LLMs. We are looking for researchers with strong expertise in one or more AI/ML research domains, such as Generative AI, LLMs, Computer Vision, Multimodal AI, Reinforcement Learning, Recommendation Systems, Search, Speech and Audio AI, Time-Series Modeling, Graph ML, Robotics, or other emerging areas.\nYou are not expected to have expertise across all these domains. Instead, you should have deep research expertise in at least one area and be capable of applying strong AI/ML fundamentals to new and interdisciplinary problems.\nYou will investigate new techniques, reproduce and extend research, formulate hypotheses, build experimental prototypes, conduct rigorous evaluations, and translate promising research into practical AI capabilities.\nThe ideal candidate is a research-oriented AI/ML professional who can go beyond integrating existing APIs and demonstrate the ability to identify research problems, implement ideas from papers, design rigorous experiments, publish high-quality research, and translate research into real-world impact.\nWhat You'll Do\nAI Research & Experimentation\nIdentify and investigate meaningful research problems in your area of AI/ML specialization.\nResearch emerging techniques across AI, machine learning, deep learning, and related fields.\nStudy, reproduce, and extend ideas from recent research papers.\nFormulate research questions, hypotheses, experiments, and evaluation methodologies.\nDesign and implement novel algorithms, architectures, and AI techniques.\nBuild experimental prototypes to validate research ideas.\nConduct systematic experiments, ablation studies, and comparative evaluations.\nAnalyze experimental results and identify opportunities for improvement.\nInvestigate methods for improving model accuracy, efficiency, robustness, generalization, and reliability.\nDocument research findings and communicate technical insights to engineering and product teams.\nResearch Specialization\nCandidates should have strong research expertise in at least one AI/ML domain, including but not limited to:\nLarge Language Models and Generative AI\nComputer Vision\nMultimodal AI\nNatural Language Processing\nReinforcement Learning\nRepresentation Learning\nRecommendation Systems\nSearch and Information Retrieval\nGenerative Models\nAI Agents and Autonomous Systems\nSpeech and Audio AI\nTime-Series Modeling and Forecasting\nGraph Machine Learning\nRobotics and Embodied AI\nOptimization and Learning Algorithms\nOther emerging AI/ML research areas\nDeep expertise in one specialization is preferred over superficial knowledge across multiple areas.\nNo candidate is expected to be an expert in all of the above domains.\nFoundation Models and Generative AI\nWhere relevant to your research specialization:\nResearch and experiment with foundation models and modern neural architectures.\nExplore LLMs, transformer architectures, multimodal models, and generative models.\nInvestigate prompting, fine-tuning, instruction tuning, model adaptation, distillation, and efficient inference.\nResearch Retrieval-Augmented Generation, semantic retrieval, and knowledge systems.\nExplore AI agents, tool use, planning, memory, reasoning, and multi-agent architectures.\nEvaluate open-source and proprietary models for specific applications.\nDevelop techniques to improve factuality, reasoning, robustness, and reliability.\nCandidates specializing in other AI/ML domains are not expected to have expertise in all Generative AI techniques listed above.\nComputer Vision and Multimodal AI\nFor candidates specializing in vision or multimodal AI:\nResearch computer vision and visual representation learning techniques.\nExplore image understanding, detection, segmentation, classification, generation, and visual reasoning.\nResearch vision-language and multimodal foundation models.\nDevelop multimodal retrieval, reasoning, and generation systems.\nExplore image, video, and cross-modal understanding.\nModel Training and Evaluation\nBuild datasets and experimental pipelines for model training, fine-tuning, and evaluation.\nDesign appropriate benchmarks, metrics, and evaluation methodologies for your research domain.\nPerform quantitative and qualitative analysis of model performance.\nConduct error analysis and investigate model failure modes.\nPerform ablation studies and controlled experiments.\nCompare approaches across relevant quality, computational, latency, scalability, and efficiency metrics.\nDevelop reproducible research and evaluation pipelines.\nApplied AI Research\nTranslate research concepts into practical AI capabilities and prototypes.\nResearch intelligent systems for problems such as search, recommendation, prediction, classification, reasoning, optimization, perception, and decision-making.\nDevelop domain-specific AI systems.\nIdentify promising academic research and evaluate its applicability to real-world problems.\nCollaborate with AI Engineering, Software Engineering, Product, and Platform teams to transition successful research prototypes into production.\nRobust and Reliable AI\nDepending on the candidate's research domain:\nInvestigate AI robustness, reliability, interpretability, and explainability.\nEvaluate models for failure modes, hallucinations, bias, adversarial behavior, and other risks.\nDevelop appropriate evaluation methodologies and guardrails.\nResearch methods for improving model transparency and reliability.\nContribute to responsible AI research practices.\nResearch and Publications\nIdentify novel and impactful research problems.\nDevelop research hypotheses and novel approaches.\nPublish research in high-quality journals and conferences.\nPresent research findings to internal and external technical audiences.\nTrack developments from leading AI research labs, conferences, journals, and open-source communities.\nContribute to research publications, patents, technical reports, and open-source projects where applicable.\nRequired Qualification\nMinimum 3 peer-reviewed research publications in Q1 journals and/or A conferences, through any qualifying combination. *\nThe publications must represent substantive research contributions in Artificial Intelligence, Machine Learning, Deep Learning, Computer Science, or a closely related field.\nCandidates will be evaluated primarily on the quality, relevance, originality, and depth of their research contributions.\nWhat We Value\nWe value researchers who can demonstrate the following research capability:\nResearch Problem → Hypothesis → Novel Approach → Implementation → Rigorous Experimentation → Evaluation → Publication → Practical Impact\nThe successful candidate does not need to be an expert in every area of AI.\nWe are looking for strong researchers with deep expertise in a particular AI/ML domain who can contribute to a broader AI research organization, collaborate across disciplines, and learn adjacent technologies when required.","description_format":"text","description_chars":7194,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Advertising","Creative 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