Manager, AI Engineering
Acadia Pharmaceuticals
16 days ago
Remote friendly (San Diego, CA)
United States
IT
Position Summary
Manager, AI Engineering advances enterprise AI and analytics capabilities by designing, building, and deploying scalable AI/ML and GenAI solutions that drive measurable business impact across R&D, Commercial, and Corporate.
Primary Responsibilities
- Design, develop, validate, and deploy machine learning, statistical, and GenAI solutions.
- Contribute to the enterprise AI strategy/roadmap (feasibility, value hypotheses, architecture, build-vs-buy).
- Build and maintain scalable ML and LLM pipelines (data ingestion to production) using ML Ops/LLM Ops standards: versioning, evaluation, observability, rollback.
- Partner with business, analytics, IT, and 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 enterprise architecture.
- Support AI governance: model documentation, lineage, risk assessment, bias testing, explainability, and applicable regulatory compliance.
- Provide technical input into AI platform/vendor evaluations (RFI/RFP; assess cost, security, data residency).
- Create reusable patterns, reference implementations, and technical documentation to accelerate adoption.
- Apply policies for AI lifecycle management, bias/robustness testing, explainability, human oversight, incident response; map AI controls to major frameworks (e.g., NIST AI RMF, EU AI Act readiness).
- Ensure data science work complies with global AI regulations, ethical standards, and applicable GxP processes.
- Participate in AI Governance Council activities as requested.
Education/Experience/Skills
- Master’s or PhD in Data Science/Statistics/CS/Math (or related) or equivalent experience.
- 5+ years applied data science/ML (model development, deployment, production support).
- Advanced Python; scikit-learn, TensorFlow, PyTorch, SQL.
- ML Ops/LLM Ops experience (registries, version control, evaluation benchmarks, monitoring).
- Experience with LLMs/GenAI, vector databases, or agent frameworks.
- Regulated environment experience; compliance with GxP or similar.
- Ability to travel domestically/internationally.
Benefits (US-based employees)
- Competitive base + discretionary bonus and equity; medical/dental/vision; employer-paid life/disability/business travel/EAP; 401(k) match 1:1 up to 5%; ESPP; 15+ vacation days; 13–15 paid holidays; paid sick time; paid parental leave; tuition assistance.
Salary Range
$122,000–$152,600 USD
Manager, AI Engineering advances enterprise AI and analytics capabilities by designing, building, and deploying scalable AI/ML and GenAI solutions that drive measurable business impact across R&D, Commercial, and Corporate.
Primary Responsibilities
- Design, develop, validate, and deploy machine learning, statistical, and GenAI solutions.
- Contribute to the enterprise AI strategy/roadmap (feasibility, value hypotheses, architecture, build-vs-buy).
- Build and maintain scalable ML and LLM pipelines (data ingestion to production) using ML Ops/LLM Ops standards: versioning, evaluation, observability, rollback.
- Partner with business, analytics, IT, and 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 enterprise architecture.
- Support AI governance: model documentation, lineage, risk assessment, bias testing, explainability, and applicable regulatory compliance.
- Provide technical input into AI platform/vendor evaluations (RFI/RFP; assess cost, security, data residency).
- Create reusable patterns, reference implementations, and technical documentation to accelerate adoption.
- Apply policies for AI lifecycle management, bias/robustness testing, explainability, human oversight, incident response; map AI controls to major frameworks (e.g., NIST AI RMF, EU AI Act readiness).
- Ensure data science work complies with global AI regulations, ethical standards, and applicable GxP processes.
- Participate in AI Governance Council activities as requested.
Education/Experience/Skills
- Master’s or PhD in Data Science/Statistics/CS/Math (or related) or equivalent experience.
- 5+ years applied data science/ML (model development, deployment, production support).
- Advanced Python; scikit-learn, TensorFlow, PyTorch, SQL.
- ML Ops/LLM Ops experience (registries, version control, evaluation benchmarks, monitoring).
- Experience with LLMs/GenAI, vector databases, or agent frameworks.
- Regulated environment experience; compliance with GxP or similar.
- Ability to travel domestically/internationally.
Benefits (US-based employees)
- Competitive base + discretionary bonus and equity; medical/dental/vision; employer-paid life/disability/business travel/EAP; 401(k) match 1:1 up to 5%; ESPP; 15+ vacation days; 13–15 paid holidays; paid sick time; paid parental leave; tuition assistance.
Salary Range
$122,000–$152,600 USD