Senior Applied ML Scientist — GenAI & Multi-Agent Systems

Archer; Palo Alto (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Archer, in collaboration with JP Morgan Chase, seeks an experienced ML leader to architect and deploy end-to-end machine learning systems for NLP, information retrieval, and GenAI in production. You will design agentic AI workflows, implement RLHF-based improvements, and scale LLMs with PEFT and distillation.

collaboration with business, technology, and governance teams is essential to deliver measurable outcomes.

Qualifications

• MS with 7 years of hand-on industry experience in building and deploying ML systems (NLP/IR/RecSys) in production
• PhD with 4 years of industry experience in ML systems is acceptable in lieu of MS
• Strong understanding of transformer architectures, distillation, transfer learning, and representation learning
• Experience with GenAI, LLMs, and fine-tuning for practical use cases (prompt design, grounded generation, RAG)
• Experience scaling LLM systems (caching, batching, governance, evaluation harnesses)
• Solid ML, DL, and statistical modeling foundations; model evaluation and error analysis
• Proficiency in Python and ML libraries; production-quality coding
• Ability to collaborate cross-functionally with product, engineering, and risk/compliance partners

Responsibilities

• Lead and deploy ML systems across NLP, IR, and GenAI production environments
• Design agentic AI systems for multi-step workflows with tool calling and multi-agent orchestration
• Apply RL/RLHF approaches to improve personalization and dialog policies
• Fine-tune and deploy SLMs/LLMs using PEFT, distillation, and quantization
• Set up experiments with robust baselines and meaningful metrics to evaluate models
• Collaborate with Business, Technology, Product, Design, Analytics and Governance teams
• Ensure responsible AI usage, privacy, safety, and security controls across GenAI systems
• Monitor and evaluate performance, latency, cost, and production constraints
• Deposit governance for supervision, safety scoring, and auditability

Skills

NLP/IR/RecSys in production

GenAI & LLM fine-tuning

Python & ML frameworks

Cross-functional collaboration

Experiment design & evaluation

Safety & governance

RL/RLHF concepts

Production-grade coding

Education

MS in a relevant field

PhD in a relevant field

Tools

PyTorch/TensorFlow

Hugging Face

scikit-learn

CUDA / GPUs

Job description

Archer, in collaboration with JP Morgan Chase, seeks an experienced ML leader to architect and deploy end-to-end machine learning systems for NLP, information retrieval, and GenAI in production. You will design agentic AI workflows, implement RLHF-based improvements, and scale LLMs with PEFT and distillation.

collaboration with business, technology, and governance teams is essential to deliver measurable outcomes.