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1–2 jobs· newest first

Research Engineer, Privacy and AnonymizationNew
Pragmatike · San Francisco, US+1 more
Full-time
Senior Machine Learning Engineer
Wiser Solutions · Remote, Canada
CA$160K–CA$200KRemote · Full-time · Senior

+10K new in the last hour

Research Engineer, Privacy and Anonymization

Pragmatike · Posted Nov 12, 2026

San Francisco, California, United States +1 more
Full-time

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Build systems to detect and remove personally identifiable information and other sensitive content from raw data before it enters processing and synthetic data pipelines.

Locations (2)

San Francisco, California, United States
Singapore, Singapore

Responsibilities

Build systems to detect PII, quasi-identifiers, credentials and other sensitive information
Develop and benchmark detection approaches that combine rules, statistical models, classifiers and LLM-based methods
Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation or synthetic data generation
Create evaluation frameworks that measure privacy risk and retained data utility
Design systems that stay robust to new data sources, schema drift, unusual formats and sensitive information embedded in unexpected fields

Requirements

Nice to have: privacy-enhancing technologies
Nice to have: differential privacy
Nice to have: k-anonymity
Nice to have: secure aggregation
Nice to have: format-preserving encryption

Skills

Python information extraction named-entity recognition classification machine-learning systems data-processing pipelines experimental instincts recall precision latency cost downstream data utility

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Senior Machine Learning Engineer

Wiser Solutions · Posted Nov 6, 2026

Remote, Canada
Remote
Full-time · Senior
CA$160K–CA$200K per year
7+ years

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Senior ML Engineer to build customer-facing AI platform and deliver production ML systems

Compensation

For this position, our hiring range for base annual pay is estimated to be CAD $160-200k at the time of this posting

Responsibilities

Take customer problems from idea to working prototype quickly, prove or kill an approach with data, and move what works into production
Train, fine-tune, and evaluate models for matching, classification, and extraction problems
Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS
Own the full ML lifecycle: experiment tracking, model versioning, deployment, monitoring, and retraining
Build evaluation that tells us whether something works: labeled golden sets, offline metrics (precision, recall), LLM-as-judge where it fits, and shadow or A/B testing before rollout

Requirements

Nice to have: LLM Fine-tuning
Nice to have: Distillation
Nice to have: Quantization
Nice to have: Caching
Nice to have: Tiered Model Routing

Skills

Python Machine Learning Large Language Models

Experiment Tracking

Model Versioning

SageMaker Pipelines

Weights & Biases

Data Engineering

Feature Engineering

Experiment Design

Class Imbalance

Model Evaluation

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