Machine Learning Engineer

📋 Job Overview

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.

🏢 About Sunset

We have scaled from $0 to a multi-eight-figure run rate in a matter of months. We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund. We are small enough that you will carry outsized responsibility and grow as quickly as the company does. You will partner with and build for some of the fastest and most important companies in the world. You will help build a massive, category-defining business from the ground floor.

🎯 The Role

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. 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.

✅ Key Responsibilities

• 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
• Partner with Applied Science on measurement and calibration, Data and Product Engineering on pipeline and review systems, and Security and Quality on acceptable risk
• Use AI engineering tools deeply to accelerate research, implementation, error analysis, and evaluation while verifying their output

📌 Required Qualifications

• You have 3+ years of professional machine learning or software engineering experience, including improving models in production
• You have startup experience, enjoy broad ownership, and thrive when requirements are evolving or incomplete
• You use modern AI tools fluently and verify their output
• You have personally moved model quality through error analysis, data work, experimentation, implementation, deployment, and iteration
• You have a strong grasp of precision, recall, F1, calibration, thresholding, class imbalance, imperfect labels, distribution shift, and representative evaluation
• 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 have a bias toward action while maintaining scientific and engineering rigor
• You are curious

⭐ Desirable Experience

• Experience with enterprise data and privacy regulations
• Experience with large-scale data processing and distributed systems
• Experience with cloud platforms (AWS, GCP, Azure)
• Experience with MLOps tools and frameworks

🎁 Benefits

Competitive salary, equity, and benefits package. Specific details will be discussed during the interview process.

🛂 Visa & Eligibility

U.S. work authorization required.