Senior Machine Learning Engineer

Role Description

This is a role for someone who trains models. Not someone who calls them. We are building classification and detection systems from scratch:

• Fine-tuned transformers
• Entity detection over structured and unstructured content
• Behavioral scoring against a taxonomy we defined ourselves

If you have owned a classifier in production, from data through training through evaluation through the retraining loop, please consider applying.

What You Would Own

• The behavioral classification model: multi-class classification of AI agent query intent against our internal taxonomy, producing labels, confidence, and supporting evidence rather than a bare score
• PII and PHI detection over query content and returned data, including span-level identification suitable for audit evidence
• Behavioral risk scoring, combining deterministic request signals with model-derived signals
• Model training, serving, and versioning on SageMaker within the asynchronous inspection path
• The evaluation harness, drift detection, and retraining loop, with a co-op supporting the harness work
• The classification output contract that downstream audit, enrichment, and dashboard consumers depend on

Qualifications

• Production ML ownership: models you trained, deployed, monitored, and retrained in a live system
• Transformer fine-tuning for text classification (BERT family, DistilBERT, or equivalent)
• Sequence labelling or named entity recognition for structured entity detection
• Python, PyTorch, Hugging Face Transformers
• Evaluation rigour: you can explain how you chose thresholds and what you traded away
• Comfort working from a written design spec rather than waiting for direction
• Authorised to work in Canada or the United States, located in British Columbia or the Seattle area

Requirements

• Bootstrapping labels through weak supervision, LLM-assisted labelling, or active learning
• Anomaly or behavioural detection with sparse or absent labels
• SageMaker training jobs and inference endpoints
• Regulated domain experience: HIPAA, PCI DSS, GDPR, or SOC 2
• Fraud, abuse, or security detection background
• Distillation or quantisation to hold an inference latency budget
• Kinesis, Kafka, or other streaming pipelines
• SQL and query structure parsing
• Mentoring a junior engineer or co-op

Who Does Well Here

• You have shipped a model that other systems depended on, and you remember what broke
• You are honest about model limitations rather than defensive, because our customers are auditors and regulators
• You can read an architecture document, disagree with part of it, and say so with a reason
• You are comfortable being the only person in the company who understands this layer, and you document accordingly
• You want ownership more than you want a large team

The Stack

• Intelligence layer: PyTorch, Hugging Face Transformers, SageMaker training and inference, evaluation and drift tooling
• Infrastructure: AWS, EKS, Terraform, Helm, Prometheus, Grafana

Location and Compensation

• Seattle area or British Columbia. Remote within those regions, with periodic in-person time with the team
• Existing authorization to work in United States is required. We can sponsor work visa and PR in Canada
• Below-market base plus meaningful early-stage equity. We discuss specific numbers early in the process rather than making you guess, and we will not ask you to name a figure first