Manager, AI Engineering
Acadia Pharmaceuticals
4 hours ago
Remote friendly (South San Francisco, CA)
United States
IT
Position Summary
- Manager, AI Engineering will design, build, and deploy scalable AI/ML and GenAI solutions delivering measurable business impact across R&D, Commercial, and Corporate; contribute to enterprise AI strategy and responsible AI governance; operationalize analytics and machine learning at scale globally.
Primary Responsibilities
- Design, develop, validate, and deploy ML, statistical, and GenAI solutions for complex enterprise problems.
- Support the enterprise AI strategy/roadmap with technical input (use-case feasibility, value hypotheses, architecture, build-vs-buy).
- Build and maintain scalable ML/LLM pipelines (data ingestion to production) using ML Ops/LLM Ops practices (versioning, evaluation, observability, rollback).
- Partner with business/analytics/IT/security to identify, prototype, and deliver high-value AI use cases.
- Evaluate and integrate AI/GenAI platform components (model endpoints, vector databases, agent frameworks, guardrails) per architecture standards.
- Support AI governance (documentation, lineage, risk assessment, bias testing, explainability, regulatory compliance).
- Provide technical input for AI platform/vendor evaluations (RFI/RFP; cost, security, data residency).
- Enable adoption via reusable patterns, reference implementations, and technical documentation.
- Uphold AI lifecycle management (bias/robustness, explainability, human oversight, incident response) and map AI controls to major frameworks.
- Ensure work complies with global AI regulations, ethical standards, and applicable GxP processes.
Education/Experience/Skills
- Master’s or PhD in Data Science/Statistics/CS/Math or related field (or equivalent experience).
- 5+ years applied data science and ML (development, deployment, production support).
- Advanced Python; scikit-learn, TensorFlow, PyTorch, SQL.
- ML Ops/LLM Ops experience (model registries, version control, evaluation benchmarks, monitoring).
- Experience with LLMs/GenAI, vector databases, and/or agent frameworks.
- Regulated environment experience; GxP or similar compliance.
- Ability to travel domestically/internationally.
Benefits (as stated)
- Medical, dental, vision; employer-paid life/disability/EAP; 401(k) match 1:1 up to 5%; ESPP; paid time off (15+ vacation days, 13–15 paid holidays, 10 days sick time); paid parental leave; tuition assistance.
Salary Range
- $122,000–$152,600 USD.
- Manager, AI Engineering will design, build, and deploy scalable AI/ML and GenAI solutions delivering measurable business impact across R&D, Commercial, and Corporate; contribute to enterprise AI strategy and responsible AI governance; operationalize analytics and machine learning at scale globally.
Primary Responsibilities
- Design, develop, validate, and deploy ML, statistical, and GenAI solutions for complex enterprise problems.
- Support the enterprise AI strategy/roadmap with technical input (use-case feasibility, value hypotheses, architecture, build-vs-buy).
- Build and maintain scalable ML/LLM pipelines (data ingestion to production) using ML Ops/LLM Ops practices (versioning, evaluation, observability, rollback).
- Partner with business/analytics/IT/security to identify, prototype, and deliver high-value AI use cases.
- Evaluate and integrate AI/GenAI platform components (model endpoints, vector databases, agent frameworks, guardrails) per architecture standards.
- Support AI governance (documentation, lineage, risk assessment, bias testing, explainability, regulatory compliance).
- Provide technical input for AI platform/vendor evaluations (RFI/RFP; cost, security, data residency).
- Enable adoption via reusable patterns, reference implementations, and technical documentation.
- Uphold AI lifecycle management (bias/robustness, explainability, human oversight, incident response) and map AI controls to major frameworks.
- Ensure work complies with global AI regulations, ethical standards, and applicable GxP processes.
Education/Experience/Skills
- Master’s or PhD in Data Science/Statistics/CS/Math or related field (or equivalent experience).
- 5+ years applied data science and ML (development, deployment, production support).
- Advanced Python; scikit-learn, TensorFlow, PyTorch, SQL.
- ML Ops/LLM Ops experience (model registries, version control, evaluation benchmarks, monitoring).
- Experience with LLMs/GenAI, vector databases, and/or agent frameworks.
- Regulated environment experience; GxP or similar compliance.
- Ability to travel domestically/internationally.
Benefits (as stated)
- Medical, dental, vision; employer-paid life/disability/EAP; 401(k) match 1:1 up to 5%; ESPP; paid time off (15+ vacation days, 13–15 paid holidays, 10 days sick time); paid parental leave; tuition assistance.
Salary Range
- $122,000–$152,600 USD.