Sunset Machine Learning Engineer at Sunset focused on improving de-identification systems using model-backed techniques. Responsible for measurable improvements in precision, recall, and data utility across multiple data modalities.
Responsibilities
Sunset turns sensitive internal enterprise data into de-identified datasets without destroying the structure and meaning that make the data valuable. The data does not arrive in one clean modality. It spans messages, documents, tables, files, images, metadata, and provider-specific structures, with important context distributed across all of them.
This is an applied, production-facing ML role. You will study errors, form hypotheses, build datasets and experiments, improve or replace models, and ship the result into a live pipeline. Evaluation, reproducibility, observability, and safe releases matter because they let us identify, ship, and verify meaningful model improvements in production.
Own and improve NER, entity resolution, structured or tabular detection, document understanding, semantic review, or related de-identification systems
Transform model failures and capability ceilings into a prioritized improvement roadmap
Design active-learning loops that combine model sweeps, LLM-assisted review, clustering, and uncertainty signals to identify the examples most worth hand-labeling
Build representative datasets and benchmarks, and use decision-relevant metrics to reveal strengths, weaknesses, uncertainty, and failure costs
Choose and combine deterministic rules, classical ML, fine-tuning, embeddings, multimodal models, and LLM-based approaches based on the problem and evidence
Design experiments, tune thresholds, analyze precision-recall and utility tradeoffs, and explain which changes are real, uncertain, or limited to particular conditions
Productionize improvements with reproducible artifacts, evaluation evidence, runtime instrumentation, and safe rollout
Optimize inference cost, latency, and throughput without hiding regressions in quality or high-risk recall
Build high-fidelity evaluation environments with seeded failure modes and programmatic verifiers that expose subtle regressions
Build reliable model- or agent-based harnesses with bounded behavior and explicit output verification when the problem calls for them
Qualification
You are an applied engineer firstThis Role May Not Be for You IfYou want data preparationExperience with NER
Required
You use modern AI tools fluently and verify their output
You are an applied engineer first: a strong Python and software engineer who can work inside data pipelines and production systems, not only notebooks
You are curious and stay current with relevant state-of-the-art methods
You choose techniques based on the shape of the problem and can combine deterministic, statistical, neural, and LLM-based approaches
You communicate uncertainty and tradeoffs clearly to scientists, engineers, and people making delivery or risk decisions
This Role May Not Be for You If
You want to focus on research novelty without owning measurable production improvement
You prefer optimizing one aggregate benchmark without investigating consequential failure modes, data segments, and failure costs
You want data preparation, evaluation, deployment, and production diagnosis to belong entirely to other teams
You reach for a larger model before understanding the errors, constraints, and simpler alternatives
You do not want AI tools to be part of your daily engineering and research workflow
Experience with NER, entity resolution, information extraction, document understanding, multimodal systems, or privacy-preserving ML
