Senior ML Engineer
Tasks
- Add guardrails for shipping AI features safely
- Build LLM powered product features with prompt engineering and retrieval
- Build ML Ops platform for model registry feature pipelines and deployment pathways
- Build and maintain small specialized classifiers for intent and abuse
- Create eval frameworks for applied AI features
- Design inference infrastructure for production load
- Design rollback and progressive rollout patterns for ML
- Develop evaluation infrastructure to detect regressions
- Develop selective fine tuning workflows
- Implement cost and latency budget management
- Implement drift detection and online evaluations
- Make build vs buy decisions for model and infrastructure choices
- Monitor cost and latency for ML systems
- Own machine learning and AI engineering layer end to end
- Set up human in the loop feedback loops
Perks/Benefits
- 401k match
- Cell phone reimbursement
- HSA contributions
- Health, dental, vision insurance
- Holidays
- Home office stipend
- Internet reimbursement
- Parental leave
- Unlimited PTO
Skills/Tech-stack
AWS | Abuse detection | Containers | Cost monitoring | Data Governance | Docker | Drift Detection | Evaluation Frameworks | Feature Engineering | Fine Tuning | Human-in-the-loop | Infrastructure as Code | Intent Classification | Language Models | Large Language Models | Latency monitoring | MLOps | Machine Learning | Machine Learning Engineering | Model Deployment | Model Registry | Online evaluation | PII handling | Prompt engineering | Python | Retrieval-Augmented Generation | Ruby on Rails | The Loop | “as-code”
Education
N/A
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