Machine Learning Engineer

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

Model improvements generalize beyond the examples used to develop them and hold up in replay, shadow, and production evidence

Priority modalities and entity classes show credible improvements in precision, recall, F1, or other decision-relevant quality measures

High-risk misses decline without unacceptable over-redaction or loss of useful structure

New formats and modalities can be covered without relying on brittle one-off fixes

Improvements reduce meaningful delivery risk, review or rework burden, or loss of data utility rather than moving only an isolated benchmark

The team can explain why a model changed, where it improved or regressed across consequential failure modes and data segments, and whether the change should ship

The path from error discovery to a trustworthy production improvement becomes faster and more repeatable

Quality gains remain inside acceptable inference-cost, latency, and operational constraints

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 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

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

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