Job description

We are looking for an Evaluation & Insights Engineer for the Human-Centered AI team to help evaluate and improve AI systems by combining data science, model behavior analysis, and qualitative insights. In this role, you will analyze AI outputs, develop evaluation frameworks, design qualitative, and translate findings into actionable improvements for product and engineering teams. This role blends deep technical expertise with strong analytical judgment to assess, interpret, and improve the behavior of advanced AI models. You will work cross-functionally with the Engineering and Project Managers, Product, and Research teams to ensure that AI experience is reliable, safe, and aligned with human expectations. Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field 8+ years of relevant industry experience in ML Engineering or Applied Research. Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face). Proven experience building scalable ML inference pipelines, model-evaluation workflows, and structured rating frameworks for large-scale AI systems. Strong ability to interpret unstructured model outputs (text, transcripts, embedding spaces) and synthesize qualitative findings into actionable engineering guidance and training objectives. Hands-on experience developing, fine-tuning, or evaluating LLMs, multimodal models, and NLP systems. Deep familiarity with AI quality metrics, hallucination detection techniques (e.g., SelfCheckGPT), model alignment (RLHF/DPO), and LLM-as-a-judge frameworks (e.g., G-Eval, DeepEval). Experience building internal tools or automated pipelines for ML workflows using tools like MLflow, Weights & Biases, or similar platforms. Strong familiarity with advanced prompt engineering, RAG architectures (vector databases, semantic search), and Fine-Tuning . Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.

Requirements

Must-haves

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field
8+ years of relevant industry experience in ML Engineering or Applied Research.
Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face).
Proven experience building scalable ML inference pipelines, model-evaluation workflows, and structured rating frameworks for large-scale AI systems.
Strong ability to interpret unstructured model outputs (text, transcripts, embedding spaces) and synthesize qualitative findings into actionable engineering guidance and training objectives.
Hands-on experience developing, fine-tuning, or evaluating LLMs, multimodal models, and NLP systems.
Deep familiarity with AI quality metrics, hallucination detection techniques (e.g., SelfCheckGPT), model alignment (RLHF/DPO), and LLM-as-a-judge frameworks (e.g., G-Eval, DeepEval).
Experience building internal tools or automated pipelines for ML workflows using tools like MLflow, Weights & Biases, or similar platforms.
Strong familiarity with advanced prompt engineering, RAG architectures (vector databases, semantic search), and Fine-Tuning .
Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonllm

Platform: apple
Effort: medium