Principal Applied Scientist

Accountabilities • Design and lead an end-to-end evaluation framework for planner-executor agents, covering scenario definition, user simulation, configuration, execution, grading, comparison, and insight generation.

• Define scenario schemas, grading specifications, and validation processes in collaboration with engineering teams, ensuring scenarios capture goals, personas, starting states, and expected outcomes.

• Develop an LLM-powered user simulator capable of realistic multi-turn conversations, using hidden goals and stop conditions, and validate its behavior against production interaction logs.

• Build isolated, parallel experiment execution systems with configurable runs and adapters that route model traffic through existing LLM gateways.

• Develop and implement an initial selection of optimization plug-ins, such as failure-based model cascading, cache-aware model switching, and prompt caching.

• Instrument model calls and integrate tracing systems to attribute token consumption, cache utilization, latency, and costs to individual requests.

• Establish grading methods based primarily on verified end states, with optional LLM-based evaluation of answer and planning quality.

• Calculate and report key performance indicators, including verified success rates, cost per successful task, p95 latency, and failures by pipeline stage.

• Apply rigorous statistical methods, including paired comparisons, confidence intervals, win/loss/tie analysis, and non-inferiority testing, while clearly identifying inconclusive findings.

• Translate experimental results into projected financial impact at production scale and develop cost-versus-quality analyses to guide optimization decisions.

• Build an automated insight engine that uses statistical computation for all reported metrics and LLM-generated explanations to communicate evidence-backed findings.

• Develop a locally hosted, API-first dashboard with launch, live monitoring, results, comparison, insights, and tracing capabilities, supported by straightforward deployment and integration documentation.

• Lead staged experimentation campaigns covering production baselines, model screening, optimization testing, and validation of the strongest combinations.

• Assess how well agent performance generalizes across scenario categories, potentially incorporating data analytics benchmark scenarios.

• Deliver final recommendations with confidence intervals and projected dollar impact, alongside technical reports, runbooks, and integration guides.

• Collaborate with engineers and business leaders to establish evaluation criteria, success thresholds, and acceptable quality margins, communicating findings to both technical and non-technical stakeholders.

• Set experimental standards, provide technical leadership, and mentor engineers in rigorous agent evaluation and AI-assisted development practices.

Requirements:

• Demonstrated experience with LLM and AI agent evaluation methodologies, including state-based verification, LLM-as-judge or agent-as-judge approaches, trajectory-level scoring, and user simulation.

• Hands-on experience building or evaluating production-grade planner-executor systems or other multi-step, tool-using AI agent architectures.

• Strong expertise in experimental design and statistical inference, including paired testing, non-inferiority analysis, power analysis, confidence intervals, and the ability to recognize when evidence is inconclusive.

• Experience with multi-objective optimization or cost-aware machine learning, including Pareto frontier analysis and translating technical results into projected financial savings.

• Familiarity with LLM routing, model cascades, model selection, and inference optimization techniques such as prompt and plan caching, context compaction, reasoning budgets, and cache-aware model switching.

• Understanding of LLM inference internals and the ability to evaluate the impact of optimization strategies on quality, latency, and cost.

• Experience implementing observability and tracing for LLM systems, including request-level attribution of tokens, cache usage, latency, and cost.

• Ability to develop production-quality research tooling using Python services, containerized and sandboxed execution environments, and data pipelines.

• Previous customer-facing or consulting experience, including scoping and delivering research-oriented technical engagements.

• Excellent communication skills, with the ability to explain complex technical findings, respond confidently to detailed questions, and connect results to business outcomes.

• Additional advantages include:

• Publications or open-source contributions in LLM evaluation, AI agents, or efficient inference.

• Experience developing internal dashboards or web applications that can be adopted and maintained by client engineering teams.

• Practical experience with LLM gateways and tracing platforms such as LangFuse or LangSmith.

• Background working in regulated or high-stakes enterprise environments where evaluation rigor is essential.

• A demonstrated understanding of real-world cost-quality trade-offs gained through production experience.

Benefits:

• Annual salary range of $200,000–$300,000.

• Remote work opportunity within the United States.

• Customer-facing, research-driven work with substantial ownership of technical strategy, experimentation, and implementation.

• Opportunity to shape enterprise AI evaluation practices and deliver tools that support real-world deployment decisions.

• Close collaboration with engineering teams and business stakeholders.

• Significant technical leadership and mentoring responsibilities.

• Opportunity to work on advanced AI agent architectures, inference optimization, and measurable enterprise AI performance improvements.

• The role involves approximately 20% travel within the United States, including onsite collaboration and client-related activities.

• Willingness to attend an onsite final interview and participate in onsite onboarding is expected.

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