Manager, AI Engineering
Acadia Pharmaceuticals
17 days ago
Remote friendly (Princeton, NJ)
United States
IT
Position Summary
- Design, build, and deploy scalable AI/ML and GenAI solutions delivering measurable business impact across R&D, Commercial, and Corporate; support enterprise AI strategy, responsible AI governance, and enterprise-scale analytics/ML.
Primary Responsibilities
- Develop, validate, and deploy ML, statistical, and GenAI solutions for complex enterprise problems.
- Provide technical input to AI roadmap/use-case feasibility, value hypotheses, architecture decisions, and build-vs-buy.
- Build and maintain scalable ML/LLM pipelines (data ingestion to production) following ML Ops/LLM Ops (versioning, evaluation, observability, rollback).
- Partner with business, analytics, IT, and security to prototype and deliver high-value AI use cases.
- Evaluate/integrate AI platform components (model endpoints, vector databases, agent frameworks, guardrails) per enterprise architecture.
- Support AI governance: documentation, lineage, risk assessment, bias testing, explainability, and regulatory compliance.
- Provide input to platform/vendor evaluations (RFI/RFP) assessing cost, security, and data residency.
- Create reusable patterns, reference implementations, and technical documentation to accelerate adoption.
- Uphold AI lifecycle management, bias/robustness testing, explainability, human oversight, and incident response; map AI controls to frameworks (e.g., NIST AI RMF, EU AI Act readiness).
- Ensure data science work complies with global AI regulations, ethical standards, and applicable GxP.
Education/Experience/Skills
- Master’s or PhD in Data Science/Stats/CS/Math (or equivalent).
- 5+ years applied data science/ML (development, deployment, production support).
- Advanced Python; scikit-learn, TensorFlow, PyTorch, SQL.
- ML Ops/LLM Ops (model registries, version control, eval benchmarks, monitoring).
- Experience with LLMs/GenAI, vector databases, or agent frameworks.
- Regulated environment experience; GxP or similar compliance.
- Domestic/international travel ability.
Benefits (US-based)
- Base salary plus discretionary bonus and equity; medical/dental/vision; 401(k) with 1:1 match up to 5%; ESPP; 15+ vacation days; paid holidays; 10 days sick time; paid parental leave; tuition assistance.
Salary Range
- $122,000—$152,600 USD
- Design, build, and deploy scalable AI/ML and GenAI solutions delivering measurable business impact across R&D, Commercial, and Corporate; support enterprise AI strategy, responsible AI governance, and enterprise-scale analytics/ML.
Primary Responsibilities
- Develop, validate, and deploy ML, statistical, and GenAI solutions for complex enterprise problems.
- Provide technical input to AI roadmap/use-case feasibility, value hypotheses, architecture decisions, and build-vs-buy.
- Build and maintain scalable ML/LLM pipelines (data ingestion to production) following ML Ops/LLM Ops (versioning, evaluation, observability, rollback).
- Partner with business, analytics, IT, and security to prototype and deliver high-value AI use cases.
- Evaluate/integrate AI platform components (model endpoints, vector databases, agent frameworks, guardrails) per enterprise architecture.
- Support AI governance: documentation, lineage, risk assessment, bias testing, explainability, and regulatory compliance.
- Provide input to platform/vendor evaluations (RFI/RFP) assessing cost, security, and data residency.
- Create reusable patterns, reference implementations, and technical documentation to accelerate adoption.
- Uphold AI lifecycle management, bias/robustness testing, explainability, human oversight, and incident response; map AI controls to frameworks (e.g., NIST AI RMF, EU AI Act readiness).
- Ensure data science work complies with global AI regulations, ethical standards, and applicable GxP.
Education/Experience/Skills
- Master’s or PhD in Data Science/Stats/CS/Math (or equivalent).
- 5+ years applied data science/ML (development, deployment, production support).
- Advanced Python; scikit-learn, TensorFlow, PyTorch, SQL.
- ML Ops/LLM Ops (model registries, version control, eval benchmarks, monitoring).
- Experience with LLMs/GenAI, vector databases, or agent frameworks.
- Regulated environment experience; GxP or similar compliance.
- Domestic/international travel ability.
Benefits (US-based)
- Base salary plus discretionary bonus and equity; medical/dental/vision; 401(k) with 1:1 match up to 5%; ESPP; 15+ vacation days; paid holidays; 10 days sick time; paid parental leave; tuition assistance.
Salary Range
- $122,000—$152,600 USD