About the job
LILT is at the forefront of creating a global network of domain experts dedicated to delivering high-quality AI evaluations that encompass training, benchmarking, red-teaming, and continuous model monitoring. We invite finance and investment professionals to lend their expertise to our human-in-the-loop AI evaluation workflows, utilized by top-tier enterprises and hyperscalers.
This position is tailored for individuals who possess a deep understanding of how financial, investment, and economic data informs real-world decision-making processes. You will leverage your expertise to evaluate, assess, and enhance multilingual AI systems.
Your contributions will play a vital role in enhancing the quality, safety, and readiness for deployment of our multilingual AI models.
This role features two distinct expert tracks based on experience level and scope of responsibility.
Track A: Finance & Investment AI Rater
Raters carry out structured evaluation tasks guided by clearly defined rubrics and instructions.
Responsibilities
Evaluate AI outputs related to finance, investment, and economic content.
Conduct structured scoring, comparison, classification, and judgment tasks.
Assess factual accuracy, numerical correctness, relevance, and risk.
Identify hallucinations, misleading financial guidance, unsupported claims, or regulatory concerns.
Consistently apply domain-specific financial guidelines across tasks.
Ideal Background
Professionals in finance, investment analysis, economics, accounting, auditing, or financial research.
Experience in interpreting financial statements, investment materials, market data, or economic analyses.
A keen eye for detail and proficiency in working with structured evaluation criteria.
Track B: Finance & Investment AI Evaluator (Senior Track)
Evaluators offer higher-level domain oversight and influence the evaluation processes.
Responsibilities
Validate and enhance evaluation rubrics and edge-case handling.
Perform adjudication in instances of disagreement among raters.
Conduct error analysis and qualitative reviews of model behavior.
Collaborate with LILT's research, product, and engineering teams to refine evaluation methodologies.

