Machine Learning Engineer

Machine Learning Engineer at GK Media Group | Lens About Lens Lens is building credibility infrastructure for the information economy — an AI-powered Media Objectivity Score (IQS) that makes influence visible, measurable, and auditable. Backed by $80K pre-seed and seeking a $3M seed round, we are a lean, mission-driven team building the Bloomberg Terminal for information. The Role We are looking for a Machine Learning Engineer to own the intelligence behind the IQS. The IQS is the core of everything Lens builds — a real-time, multi-dimensional credibility scoring system that is transparent, auditable, and reproducible. You will build and iterate on the ML systems that make this possible. What You'll Do Architect and implement the IQS scoring pipeline: prompt design, LLM API orchestration, and result aggregation Build multi-agent verification workflows to improve scoring accuracy and catch edge cases Design and implement score reproducibility systems: content hashing, cache keying by model version and prompt Develop and maintain fine-tuning pipelines using RLAIF on expert-validated article datasets Collaborate with the Expert Panel to translate human credibility judgments into training signals Build abuse-handling and adversarial robustness features into the scoring architecture Evaluate model performance across IQS dimensions and produce calibration reports Progress the system from frontier LLM APIs (Phase 1) toward in-house fine-tuned models (Phase 3) What We're Looking For 3+ years of applied ML or NLP engineering experience Strong familiarity with large language models and prompt engineering Experience building production inference pipelines (Anthropic, OpenAI, or equivalent APIs) Understanding of evaluation frameworks, benchmarking, and human-in-the-loop validation Proficiency in Python and ML tooling (Hugging Face, PyTorch, or equivalent) Ability to communicate technical design decisions to non-technical stakeholders Nice to Have Experience with RLHF or RLAIF fine-tuning workflows Familiarity with multi-agent architectures (LangGraph, CrewAI, AutoGen, or equivalent) Background in computational linguistics, journalism studies, or information science Experience with extended-thinking or chain-of-thought model APIs Tech Stack Python, Anthropic / OpenAI APIs, Hugging Face Transformers, multi-agent frameworks