Applied Scientist, GenAI & ML Systems

Location: Wilmington, MA (US) - Fulltime Onsite 

About the Role

We are hiring an Applied Scientist to lead the design and deployment of production-grade GenAI and ML systems with a strong emphasis on being hands-on. You will personally build, iterate, and ship systems focused on LLM/SLM optimization for agentic, multi-agent architectures in cloud environments.

This role is ideal for someone with deep expertise in one or more areas of LLM/SLM optimization for agent-based systems, and hands-on experience in designing, implementing, and operating large-scale multi-agent systems in the cloud.

Key Responsibilities

• Hands-on ownership of building and shipping multi-agent systems (planner/executor, tool-using agents, supervisor patterns, routing, role-based agents) from prototype to production.

• Write production-quality code for agent orchestration, tool integration, memory/state design, and context management.

• Lead context engineering strategies for multi-agent coordination: prompt design, state persistence, agent handoffs, grounding, constraints, and safety controls.

• Hands-on fine-tune and deploy SLM models for production usage: dataset creation, training workflows, evaluation, and inference serving.

• Build Advanced RAG pipelines end-to-end, including semantic search, embeddings, hybrid retrieval, and cross-encoder reranking.

• Implement evaluation frameworks for multi-agent systems covering quality, latency, cost, robustness, and failure mode detection.

• Collaborate with platform and product engineering to ensure solutions are cloud-native, secure, observable, and scalable (monitoring, logging, CI/CD).

• Optimize for cost and latency via model routing, caching, compression strategies, and inference efficiency improvements.

• Mentor peers through code reviews, architecture sessions, and hands-on technical leadership.

Required Knowledge & Experience

• Context engineering for complex multi-agent systems
  (prompt orchestration, tool calling, memory/state design, routing, constraint handling)

• Fine-tuning of SLMs and delivering them to production
  (training strategies, validation, deployment, monitoring, rollback readiness)

• Experience with Advanced RAG, semantic search, embeddings, and cross-encoders
  (retrieval tuning, chunking strategies, query rewriting/planning, reranking)

• Ability to translate ambiguous requirements into concrete architectures, metrics, and deliverables

• Hands-on inference optimization experience: quantization, distillation, batching, caching, model routing, speculative decoding

• Experience building retrieval systems at scale using vector DBs and search stacks

• Comfort working across the full lifecycle: research → prototype → A/B test → production hardening

Preferred Qualifications

• Familiarity with enterprise constraints: privacy, security, data governance, permissions, auditability

• Experience designing and running GenAI observability: traces, prompt/versioning, tool call logging, feedback loops

• Strong ability to implement production-quality systems in Python (and/or adjacent backend languages)

• Proven experience deploying GenAI/ML systems in cloud environments (AWS/Azure/GCP)

• Experience with scalable inference and service operations: containers, APIs, observability, reliability practices

• MS/PhD in CS/ML/NLP/Stats (or equivalent applied experience building production systems)