Applied AI Researcher

Articul8 AI

Applied AI Researcher advancing domain-specific GenAI platform through experiments and model training. Collaborating to ship robust AI solutions for enterprises in regulated industries.

Posted 5/5/2026full-timeBengaluru • 🇮🇳 IndiaSeniorLeadWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoGoogle Cloud PlatformPythonPyTorch

About the role

Key responsibilities & impact

Architect and orchestrate massively parallel AI research workflows — design experiments that leverage fleets of agentic AI systems to explore hypothesis spaces, hyperparameter landscapes, and architectural variations at a scale and speed no single researcher could achieve alone
Design, train, and iterate on models across the full GenAI stack — LLMs, VLMs, embedding models, rerankers, and reward models — using agentic pipelines that autonomously manage data preprocessing, training runs, evaluation sweeps, and result synthesis
Go deep: push the frontier of domain-specific AI — conduct rigorous, first-principles research into model architectures, training dynamics, reinforcement learning, and knowledge representation, using AI agents to accelerate literature review, ablation studies, and mathematical analysis
Go broad: span disciplines and modalities — amplify your expertise across NLP, computer vision, multimodal understanding, agentic reasoning, and domain science by delegating exploration, prototyping, and benchmarking to parallel agent systems so you can synthesize insights across fields simultaneously
Build agentic research infrastructure — develop and contribute to shared tooling, libraries, and platforms that enable every researcher on the team to orchestrate autonomous experiment pipelines, data processing workflows, and evaluation harnesses at scale
Ship research into production at velocity — collaborate with engineering, product, and domain experts to integrate breakthroughs into the platform rapidly, using agentic CI/CD and automated integration testing to compress the research-to-deployment cycle
Amplify collective intelligence — document findings, publish at top-tier venues, and build internal knowledge systems that agentic tools can index and reason over — turning every insight into a force multiplier for the entire team
Continuously raise the ceiling on human potential — proactively identify bottlenecks in your own workflow and the team's, then design or adopt efficient, scalable solutions that eliminate them — treating your own augmentation as a core research output.

Requirements

What you’ll need

Education: PhD in Computer Science, Machine Learning, or a related field; or MSc with 4+ years of post-graduation research experience.
Model development: You have trained or fine-tuned at least one neural model end-to-end — data preparation through evaluation. You understand why your model converges or doesn't, not just how to launch a training run.
Technical foundations: Strong working knowledge of probability, optimization, and linear algebra applied to at least one of: NLP, computer vision, reinforcement learning, or information retrieval. You can derive the math behind the methods you use.
Infrastructure: Experience building training or evaluation pipelines that handle real data — preprocessing, distributed computation, experiment tracking, and reproducibility.
Software engineering: Production-quality Python. You write code others can read, test, and extend. Fluent with Git and collaborative development workflows.
Preferred Qualifications:
Experience with distributed training frameworks (PyTorch DDP, DeepSpeed, FSDP) — you understand gradient synchronization and can debug multi-GPU failures.
Published at NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, or equivalent. Quality of contribution matters more than count.
Hands-on experience with post-training methods (RLHF, DPO, reward modeling) — beyond reading papers.
Practical cloud infrastructure experience (AWS, GCP, or Azure) for ML workloads — you can provision resources, manage jobs, and troubleshoot training failures.
8+ years of relevant experience in research, or equivalent experience.

Benefits

Comp & perks

Professional development opportunities

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI research workflowsmodel developmentneural model trainingprobabilityoptimizationlinear algebradistributed training frameworkspost-training methodscloud infrastructurePython

Soft Skills

collaborationdocumentationproblem-solvingworkflow optimizationknowledge sharing

Certifications

PhD in Computer ScienceMSc in Machine Learning