Experience:

• 3+ years of experience in machine learning engineering or applied AI

Responsibilities:

• Fine-tune and adapt pre-trained and open-source NLP models for PII detection, named entity recognition, and text classification
• Build and maintain the data classification pipeline that powers DiscoverIQ and ClassifyIQ
• Design and implement model evaluation frameworks to measure precision, recall, and accuracy across data types
• Optimize models for on-premise and edge deployment with small footprint, low latency, and offline-capable inference
• Optimize model inference for cloud production workloads focusing on latency, throughput, and cost efficiency
• Collaborate with product and engineering teams to integrate AI capabilities into the platform
• Research and prototype new approaches for document understanding, contextual classification, and anomaly detection
• Build training data pipelines and labeling workflows to continuously improve model quality

Additional responsibilities:

• Use AI as a force multiplier to accelerate research, experimentation, and delivery while maintaining quality with human oversight
• Work independently in a fast-moving team with strong problem-solving skills

Nice to have:

• Experience with data privacy, PII detection, or compliance-related ML applications
• Experience with model quantization, distillation, or other optimization techniques for edge deployment
• Background in building OCR or document processing pipelines
• Familiarity with LLM fine-tuning, prompt engineering, or agentic AI workflows
• Experience with Go or TypeScript for backend services

More skills:

modern ML frameworks, NLP tasks, cloud environments, on-premise deployment, edge deployment, ML fundamentals, training, evaluation, overfitting, data quality, model lifecycle