About The Role

About SunsetAt its core, Sunset was founded to help founders. We started by supporting startups through shutting down, but we have since expanded into unlocking a new revenue stream for all types of <businesses.In> 2025, we had a unique insight: the data every company generates each day through collaboration, communication, and building is some of the most valuable training data in the world. Public and synthetic data can only get frontier models so far, so the next generation of model progress depends on real, proprietary data grounded in how actual businesses operate. We are a primary source of it, partnering directly with the frontier AI labs building what comes next.Why Join Sunset NowWe have scaled from $0 to a multi-eight-figure run rate in a matter of monthsWe have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle FundWe are small enough that you will carry outsized responsibility and grow as quickly as the company doesYou will partner with and build for some of the fastest and most important companies in the worldYou will help build a massive, category-defining business from the ground floorThe RoleSunset 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.You> will improve how well our system understands and protects that data. Your initial scope will be a prioritized subset of named-entity recognition, entity and identity resolution, structured extraction, classification, semantic review, or other model-backed parts of the de-identification pipeline. We do not expect one person to be an expert in every modality. The goal is measurable improvement in the areas you own: better precision, recall, F1, high-risk coverage, and preserved data utility across the failure modes that matter.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.What You'll DoOwn and improve NER, entity resolution, structured or tabular detection, document understanding, semantic review, or related de-identification systemsTransform model failures and capability ceilings into a prioritized improvement roadmapDesign active-learning loops that combine model sweeps, LLM-assisted review, clustering, and uncertainty signals to identify the examples most worth hand-labelingBuild representative datasets and benchmarks, and use decision-relevant metrics to reveal strengths, weaknesses, uncertainty, and failure costsChoose and combine deterministic rules, classical ML, fine-tuning, embeddings, multimodal models, and LLM-based approaches based on the problem and evidenceDesign experiments, tune thresholds, analyze precision-recall and utility tradeoffs, and explain which changes are real, uncertain, or limited to particular conditionsProductionize improvements with reproducible artifacts, evaluation evidence, runtime instrumentation, and safe rolloutOptimize inference cost, latency, and throughput without hiding regressions in quality or high-risk recallBuild high-fidelity evaluation environments with seeded failure modes and programmatic verifiers that expose subtle regressionsBuild reliable model- or agent-based harnesses with bounded behavior and explicit output verification when the problem calls for themPartner with Applied Science on measurement and calibration, Data and Product Engineering on pipeline and review systems, and Security and Quality on acceptable riskUse AI engineering tools deeply to accelerate research, implementation, error analysis, and evaluation while verifying their outputWhat Success Looks LikeModel improvements generalize beyond the examples used to develop them and hold up in replay, shadow, and production evidencePriority modalities and entity classes show credible improvements in precision, recall, F1, or other decision-relevant quality measuresHigh-risk misses decline without unacceptable over-redaction or loss of useful structureNew formats and modalities can be covered without relying on brittle one-off fixesImprovements reduce meaningful delivery risk, review or rework burden, or loss of data utility rather than moving only an isolated benchmarkThe team can explain why a model changed, where it improved or regressed across consequential failure modes and data segments, and whether the change should shipThe path from error discovery to a trustworthy production improvement becomes faster and more repeatableQuality gains remain inside acceptable inference-cost, latency, and operational constraintsYou Might Thrive Here IfYou have 3+ years of professional machine learning or software engineering experience, including improving models in productionYou have startup experience, enjoy broad ownership, and thrive when requirements are evolving or incompleteYou use modern AI tools fluently and verify their outputYou have personally moved model quality through error analysis, data work, experimentation, implementation, deployment, and iterationYou have a strong grasp of precision, recall, F1, calibration, thresholding, class imbalance, imperfect labels, distribution shift, and representative evaluationYou are an applied engineer first: a strong Python and software engineer who can work inside data pipelines and production systems, not only notebooksYou have a bias toward action while maintaining scientific and engineering rigorYou are curious and stay current with relevant state-of-the-art methodsYou choose techniques based on the shape of the problem and can combine deterministic, statistical, neural, and LLM-based approachesYou communicate uncertainty and tradeoffs clearly to scientists, engineers, and people making delivery or risk decisionsThis Role May Not Be for You IfYou want to focus on research novelty without owning measurable production improvementYou prefer optimizing one aggregate benchmark without investigating consequential failure modes, data segments, and failure costsYou want data preparation, evaluation, deployment, and production diagnosis to belong entirely to other teamsYou reach for a larger model before understanding the errors, constraints, and simpler alternativesYou do not want AI tools to be part of your daily engineering and research workflowBonusExperience with NER, entity resolution, information extraction, document understanding, multimodal systems, or privacy-preserving MLExperience with hyperparameter tuning, data augmentation, model merging, ensembles, knowledge distillation, or multimodal model trainingExperience fine-tuning or adapting transformer, GLiNER, embedding, vision-language, or small specialized modelsExperience with active learning, uncertainty sampling, weak supervision, human-in-the-loop review, or LLM-assisted evaluation pipelinesExperience building goldens, adversarial corpora, replay systems, model bakeoffs, agentic harnesses, or programmatic evaluation environmentsExperience with difficult ML or labeling problemsExperience with ONNX Runtime, TensorRT, model pruning, quantization, or other CPU/GPU inference optimizationExperience with sensitive enterprise data or other high-trust production systemsExperience with synthetic data generation and managing the synth-to-real gap