The **Synthetic Data Specialist** designs, generates, validates, and operationalizes **synthetic datasets** that safely replicate the statistical and structural properties of sensitive or scarce real data. The role enables teams to train, test, and analyze AI/ML systems while reducing exposure to regulated data (e.g., PII) and accelerating development cycles through faster, safer data access.

The **Senior Synthetic Data Specialist** designs, builds, and governs synthetic data capabilities that enable machine learning development, testing, analytics, and product experimentation when real data is limited, sensitive, biased, or operationally difficult to access. This role blends applied ML, data engineering, privacy engineering, and data quality practices to produce synthetic datasets that are **useful, safe, explainable, and reproducible**.

The **Senior Search Relevance Specialist** is a senior individual contributor in the AI & ML organization responsible for ensuring that search and retrieval experiences return the most useful, accurate, and trustworthy results for users. This role blends applied machine learning, information retrieval (IR), experimentation, and product judgment to improve ranking quality across queries, intents, and user segments.

The **Senior Robotics Specialist** is a senior individual contributor in the **AI & ML** department responsible for designing, integrating, validating, and operationalizing robotics capabilities that combine perception, planning, control, and safe real-world execution. This role translates business requirements into reliable robotic behaviors and deployable autonomy software, working across simulation, edge compute, and cloud-based orchestration.

The **Senior Responsible AI Specialist** ensures that the company designs, builds, deploys, and operates AI-enabled products in a way that is **safe, fair, compliant, secure, explainable where needed, and aligned with documented governance standards**. This role translates evolving responsible AI principles and regulations into **practical engineering requirements, evaluation methods, release gates, and operational controls** that product and engineering teams can realistically execute.

The **Senior Model Evaluation Specialist** designs, executes, and operationalizes rigorous evaluation of machine learning (ML) and generative AI models to ensure they are accurate, reliable, safe, and fit for production use. This role turns ambiguous product and risk questions (“Is the model good enough?”, “Is it safe?”, “Will it regress?”) into measurable criteria, repeatable test suites, and decision-ready insights.

The **Senior Machine Learning Specialist** is a senior individual contributor responsible for designing, building, validating, and operating machine learning solutions that measurably improve software products and internal platforms. The role bridges applied research and production engineering by translating business needs into robust ML systems, ensuring models are accurate, reliable, cost-effective, and governable at scale.

The **Senior LLM Trainer** is a senior individual contributor in the **AI & ML** organization responsible for improving large language model (LLM) behavior through high-quality training data, preference signals, evaluation design, and alignment techniques (e.g., instruction tuning and RLHF-style workflows). This role sits at the intersection of product intent, linguistic/semantic quality, and ML training operations—turning ambiguous user needs and policy constraints into measurable model improvements.

The **Senior Autonomous Systems Specialist** designs, validates, and operationalizes software autonomy capabilities—planning, decision-making, and closed-loop control—so products and platforms can act reliably with minimal human intervention in dynamic environments. This role sits at the intersection of **AI/ML, real-time software engineering, simulation, and safety-oriented engineering**, converting research-grade autonomy approaches into production-grade systems with measurable reliability.

The **Senior AI Trainer** is a senior individual contributor within the **AI & ML** department responsible for improving the quality, reliability, and safety of AI model behavior by designing training data strategies, creating high-fidelity human feedback, and operationalizing evaluation and continuous improvement loops. The role sits at the intersection of product intent, language/data quality, and model development, translating business and user needs into measurable model behaviors through structured training and evaluation programs.

The **Senior AI Governance Specialist** designs, operationalizes, and continuously improves the governance system that ensures AI/ML solutions are safe, compliant, trustworthy, and fit-for-purpose across their lifecycle—from ideation through deployment and retirement. This role translates regulatory obligations, internal risk appetite, and ethical principles into actionable controls, processes, and evidence that engineering and product teams can execute without slowing delivery unnecessarily.

The **Search Relevance Specialist** is an applied search and data specialist responsible for improving the quality, usefulness, and business impact of an organization’s search experiences. This role focuses on **measuring relevance**, diagnosing ranking and retrieval issues, and implementing practical improvements across lexical and ML-based search systems (e.g., boosting, query understanding, learning-to-rank, vector search tuning, and evaluation frameworks).

The **Robotics Specialist** designs, integrates, and operationalizes robotics software capabilities—spanning perception, planning, control, simulation, and fleet operations—so robotic systems can perform reliably in real-world environments. This is an **individual contributor (IC)** specialist role, typically mid-level, positioned in an **AI & ML department** within a software company or IT organization that develops and/or operates robotics-enabled products, platforms, or internal automation solutions.

The Responsible AI Specialist ensures that AI/ML systems are designed, built, evaluated, deployed, and operated in ways that are trustworthy, compliant, and aligned to the company’s ethical commitments and risk tolerance. This role blends applied AI governance, technical risk assessment, and product engineering partnership to prevent harm and improve the reliability and accountability of AI features throughout the lifecycle.

The Model Evaluation Specialist designs, executes, and operationalizes rigorous evaluation of machine learning (ML) and increasingly large language models (LLMs) across offline benchmarks, pre-production testing, and post-deployment monitoring. The role exists to ensure models are **measurably effective, safe, reliable, and aligned with product intent**, and that model quality is assessed consistently over time as data, prompts, and user behavior evolve.

The **Machine Learning Specialist** designs, builds, evaluates, and operationalizes machine learning solutions that deliver measurable product and business outcomes in a software or IT organization. This role focuses on translating well-scoped business problems into reliable ML systems, partnering closely with engineering, data, and product teams to move models from experimentation into production with appropriate monitoring and governance.

The **LLM Trainer** is a specialist individual contributor responsible for improving the usefulness, safety, and reliability of large language model (LLM) behavior through high-quality training data creation, annotation, preference/ranking workflows (e.g., RLHF-style data), evaluation design, and systematic error reduction. The role sits at the intersection of **applied AI**, **data operations**, and **model quality**, turning ambiguous product expectations (“be helpful and safe”) into measurable training signals and repeatable processes.

The **LLM Evaluation Specialist** designs, runs, and operationalizes evaluation systems that measure the quality, safety, and business fitness of Large Language Model (LLM) capabilities used in products and internal platforms. The role exists to ensure that LLM-powered features are **measurable, comparable, reliable in production**, and aligned with user needs and organizational risk posture—especially as models, prompts, tools, and data change rapidly.