Position Details

Type Full-Time

Experience mid

Exp. Years 2 years (PhD) or 4 years (MS) + Applied Research

Education PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields (or MS with degree obtained by start date + 2 years)

Category AI & Machine Learning

About this role

Applied Researcher II focused on AI foundations and LLMs to reimagine banking experiences. Partners with data scientists, engineers, and product managers to translate state-of-the-art AI into customer-facing products and scalable infrastructure.

Key Responsibilities

Partner with cross-functional teams to deliver AI-powered products
Build AI foundation models through design, training, evaluation, and implementation
Apply state-of-the-art AI to customer experiences
Translate complex research into business goals
Own research agenda and long-running projects

Technical Overview

Stack includes PyTorch, Huggingface, Lightning, AWS Ultraclusters, VectorDBs; develops AI foundation models from design to deployment; emphasizes training optimization, RLHF, and open-source tooling.

Ideal Candidate

The ideal candidate is a mid-senior applied AI researcher with a PhD/MS in a quantitative field, strong track record in training large language models, and publications. They should be proficient with PyTorch, Huggingface, and cloud ML tooling, and capable of delivering research to production in collaboration with product and engineering teams.

Must-Have Skills

PhD in Electrical EngineeringComputer EngineeringComputer ScienceAIMathematicsor related fields with degree to be obtained by start date + 2 years (or MS in related fields with 4 years of experience in Applied Research)

Nice-to-Have Skills

PhD in Computer ScienceMachine LearningComputer EngineeringApplied MathematicsElectrical Engineering or related fieldsLLMPhD focus on NLP or Masters with 5 years of industrial NLP research experiencePublications in ACL/NAACL/EMNLP/NeurIPS/ICML/ICLRExperience training a large language model (10B+ parameters)Optimization (Training & Inference)Model sparsification/quantization/training parallelismDeploying fine-tuned LLMsCompiler design

Required Skills

PhD in Electrical EngineeringComputer EngineeringComputer ScienceAIMathematicsor related fields; experience building large deep learning models; training optimization; self-supervised learning; robustness; explainability; RLHF; publications; open-source tooling; Python; PyTorch; Huggingface; Lightning; VectorDBs; NLP; LLM; Kubernetes; Kubeflow; Apache Airflow

Hard Skills

PythonPyTorchHuggingfaceLightningVectorDBsNLPLLMTraining optimizationSelf-supervised learningRobustnessExplainabilityRLHFOpen-source toolingKubernetesKubeflowApache Airflow

Soft Skills

interpersonal skillscommunicationinitiativeleadershipteam collaboration

Industry & Role

Industry Banking

Job Function Research and operationalize AI foundation models for innovative banking experiences

Role Subtype Data scientist / research scientist

Tech Domains Python, PyTorch, Huggingface, Tableau, Kubernetes, Kubeflow, Apache Airflow

Clearance & Visa

Visa Sponsorship Yes

Keywords for Your Resume

Applied Researcher IIAI FoundationsLLM CoreAgentic AIPyTorchAWS UltraclustersHuggingfaceLightningVectorDBsNLPLLMTrainingEvaluationValidationRLHFSelf-supervised learningRobustnessExplainabilityKubernetesKubeflowApache AirflowOpen-source toolingPublicationsResearch agendaapplied researchllm coreai foundationspytorchhuggingfacelightningvectordbsnlpllmtraining optimization

Deal Breakers

Lack of required degree or years of applied research experience, No experience with large language models or NLP, No publications in major ML/NLP conferences

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