Architect ML - AI Researcher
Remote-first opportunity within the United States.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 10+ years of experience in machine learning, data science, or AI engineering
- Required Skills
- PythonSQLCloud ComputingPyTorchscikit-learnMLOpsGenerative AI
Requirements
- 10+ years of experience in machine learning, data science, or AI engineering, with at least 1+ year in healthcare-focused ML/AI environments.
- Advanced degree (PhD with 1+ year experience or Master’s with 4+ years of experience) in Computer Science, AI, Data Science, or related fields.
- Strong hands-on expertise in Python, SQL, and ML frameworks such as PyTorch, Scikit-learn, Pandas, NumPy, and LightGBM.
- Proven experience deploying ML systems in production SaaS environments using cloud platforms such as AWS, Azure, or Google Cloud.
- Deep understanding of transformer architectures, LLMs, and techniques such as RAG, PEFT (LoRA/QLoRA), prompt tuning, and agentic frameworks (e.g., LangChain, LlamaIndex).
- Experience working with healthcare data formats such as EHR, ADT, and clinical notes.
- Strong background in distributed systems, CI/CD pipelines, MLOps, and scalable AI system design.
- Excellent communication and leadership skills, with experience mentoring teams and leading complex, multi-stakeholder projects.
- Ability to bridge research and business impact, translating technical outputs into actionable insights and measurable outcomes.
Responsibilities
- Lead the design and architecture of scalable ML/AI systems, integrating GenAI and LLM-based solutions into cloud-native SaaS platforms, particularly in healthcare contexts.
- Develop, evaluate, and optimize machine learning models for use cases such as prediction, summarization, classification, semantic search, and clinical decision support.
- Conduct applied research, experimentation, and model evaluation using modern frameworks and responsible AI practices.
- Build and oversee data pipelines and ML workflows leveraging large-scale datasets and big data technologies such as Spark, Databricks, or cloud data lakes.
- Collaborate with global engineering, data science, and clinical teams to translate business and healthcare requirements into robust technical solutions.
- Lead architectural discussions with clients and stakeholders, providing technical direction, troubleshooting guidance, and strategic recommendations.
- Mentor engineering teams and contribute to best practices in ML system design, deployment, and lifecycle management.
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