Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Machine Learning Engineer role.

Rezi rewrites your resume against Sunset's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Machine Learning Engineer posting at Sunset — free, in seconds.

About the Role

Sunset transforms sensitive enterprise data into de-identified datasets without compromising structure or meaning. This role focuses on improving the system's understanding and protection of data, with an initial scope in areas like named-entity recognition, entity resolution, structured extraction, classification, semantic review, or other model-backed de-identification pipeline components. The goal is measurable improvement in owned areas, focusing on precision, recall, F1, high-risk coverage, and preserved data utility.

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

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

Skills

NER
entity resolution
structured extraction
classification
semantic review
Python
machine learning
software engineering
AI engineering tools
deterministic rules
classical ML
fine-tuning
embeddings
multimodal models
LLM-based approaches
precision
recall
F1
calibration
thresholding
class imbalance
imperfect labels
distribution shift
representative evaluation
information extraction
document understanding
privacy-preserving ML
hyperparameter tuning
data augmentation
model merging
ensembles
knowledge distillation
transformer models
GLiNER models
vision-language models
small specialized models
active learning
uncertainty sampling
weak supervision
human-in-the-loop review
LLM-assisted evaluation pipelines
goldens
adversarial corpora
replay systems
model bakeoffs
agentic harnesses
programmatic evaluation environments
difficult ML problems
labeling problems
ONNX Runtime
TensorRT
model pruning
quantization
CPU/GPU inference optimization
sensitive enterprise data
high-trust production systems
synthetic data generation
synth-to-real gap

Experience Level

3+ years of professional machine learning or software engineering experience

About the Company

Sunset was founded to help founders, initially supporting startups through shutting down, and has expanded to unlock new revenue streams for businesses.
The company's insight in 2025 was that the data generated daily through collaboration, communication, and building is valuable training data for AI models.
Sunset partners directly with frontier AI labs, providing a primary source of real, proprietary data grounded in actual business operations.
Sunset has scaled from $0 to a multi-eight-figure run rate in months.
The company has raised funding from investors including Floodgate, Afore, Ludlow, and Hustle Fund.