Machine Learning Engineer

Sunset•New York, NY

About The Position

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.

Requirements

3+ years of professional machine learning or software engineering experience, including improving models in production
Startup experience, enjoy broad ownership, and thrive when requirements are evolving or incomplete
Use modern AI tools fluently and verify their output
Personally moved model quality through error analysis, data work, experimentation, implementation, deployment, and iteration
Strong grasp of precision, recall, F1, calibration, thresholding, class imbalance, imperfect labels, distribution shift, and representative evaluation
Applied engineer first: a strong Python and software engineer who can work inside data pipelines and production systems, not only notebooks
Bias toward action while maintaining scientific and engineering rigor
Curious and stay current with relevant state-of-the-art methods
Choose techniques based on the shape of the problem and can combine deterministic, statistical, neural, and LLM-based approaches
Communicate uncertainty and tradeoffs clearly to scientists, engineers, and people making delivery or risk decisions

Nice To Haves

Experience with NER, entity resolution, information extraction, document understanding, multimodal systems, or privacy-preserving ML
Experience with hyperparameter tuning, data augmentation, model merging, ensembles, knowledge distillation, or multimodal model training
Experience fine-tuning or adapting transformer, GLiNER, embedding, vision-language, or small specialized models
Experience with active learning, uncertainty sampling, weak supervision, human-in-the-loop review, or LLM-assisted evaluation pipelines
Experience building goldens, adversarial corpora, replay systems, model bakeoffs, agentic harnesses, or programmatic evaluation environments
Experience with difficult ML or labeling problems
Experience with ONNX Runtime, TensorRT, model pruning, quantization, or other CPU/GPU inference optimization
Experience with sensitive enterprise data or other high-trust production systems
Experience with synthetic data generation and managing the synth-to-real gap

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