Senior ML Engineer
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Role details
Tech stack
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Job description
You’ll work directly with clients and cross-functional teams to build models that solve real business problems - demand forecasting, clinical document understanding, risk scoring, and intelligent process automation. Every model you build ships to production and creates measurable impact., * Design and implement end-to-end ML pipelines - ingestion, feature engineering, training, evaluation, and serving
- Build and fine-tune models for NLP, structured prediction, and time-series forecasting
- Deploy models to production with monitoring, drift detection, and automated retraining
- Collaborate with data engineers on feature stores and training data pipelines
- Evaluate and integrate LLM-based solutions where they provide clear value
- Establish best practices for experiment tracking, model versioning, and reproducibility
Requirements
Do you have experience in Production systems?, * 5+ years building and deploying ML models in production
- Strong Python and ML framework experience (PyTorch, TensorFlow, or scikit-learn)
- Cloud ML platform experience (SageMaker, Vertex AI, or Azure ML)
- Solid understanding of MLOps - CI/CD for models, monitoring, and serving infrastructure
- Comfort with messy real-world data and robust preprocessing pipelines
- Ability to explain model trade-offs to non-technical stakeholders
Benefits & conditions
Pulled from the full job description
- Work from home stipend
- Vision insurance
- Dental insurance
- Unlimited paid time off
- Conference stipend
- Flexible schedule, * Experience fine-tuning and deploying LLMs in production
- Background in healthcare, finance, or regulated industries
- Experience building RAG systems
- Contributions to open-source ML projects
What we offer
- Remote-first with async collaboration and flexible hours
- Competitive salary with equity
- Unlimited PTO
- Learning and conference budget
- Home office stipend
- Health, dental, and vision coverage
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