Applied Scientist - GenAI

TraceLink, Inc•Wilmington, MA

5h•$175,289 - $215,589•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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)

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.