Senior Manager, AI Engineering

Scorpion Therapeutics; San Diego (CA); On-site; USD 150,000 - 188,000; Full time

Benefits offered by this job

Discretionary bonus

Equity awards eligibility

Medical/dental/vision; life/disability

Job summary

Scorpion Therapeutics is seeking a Senior Manager, AI Engineering to advance enterprise AI capabilities by developing, deploying, and governing scalable AI/ML solutions across R&D, Commercial, and Corporate teams.

You will lead the design, validation, and deployment of ML models and GenAI pipelines, ensuring governance, risk management, and regulatory readiness while collaborating with IT, InfoSec, and analytics teams.

Qualifications

• MS in quantitative field with 5+ years relevant experience; or PhD with 2+ years industry in ML/AI/data science.
• Production or research experience deploying/supporting ML/AI.
• Proficiency: Python, SQL; frameworks such as PyTorch/TensorFlow/scikit-learn.
• GenAI experience: LLMs, RAG, vector databases, agent frameworks.
• ML Ops/LLM Ops experience (version control, evaluation, monitoring, deployment).
• Knowledge of responsible AI, explainability, evaluation methods, and AI governance.
• Regulated/compliance-focused environment experience preferred.
• Domestic/international travel as needed.

Responsibilities

• Execute enterprise AI strategy/roadmap; align AI/ML initiatives with business priorities and identify high-value use cases.
• Develop AI/ML solutions across R&D, Commercial, and Corporate functions.
• Deliver analytics and data science use cases for data-driven decision-making.
• Support AI governance with technical expertise on model risk, responsible AI practices, and lifecycle management.
• Assess feasibility, business value, and implementation risks for prioritization.
• Design, develop, validate, deploy, and support ML models and scalable ML/LLM pipelines using ML Ops/LLM Ops.
• Evaluate, implement, and optimize GenAI (LLMs, vector databases, endpoints, agent frameworks, guardrails) per enterprise architecture.
• Maintain model/prompt/dataset documentation for lineage, auditability, governance compliance, approvals, and system-of-record standards.
• Build reusable frameworks, reference implementations, and technical standards to improve scalability/efficiency.
• Partner with IT, Data Insights & Analytics, and InfoSec for AI platform support, infrastructure, vendor assessments, and RFI/RFPs.
• Apply governance standards (lifecycle management, bias/robustness testing, explainability, human oversight, incident response, regulatory compliance).
• Support compliance with GxP and evolving AI regulations (e.g., NIST AI RMF, EU AI Act readiness).
• Contribute to AI enablement (knowledge sharing, documentation, education, portfolio reviews).
• Monitor emerging AI/ML/regulatory developments; recommend architectures, platform strategy, and build-vs-buy.

Skills

Python

SQL

PyTorch

TensorFlow

scikit-learn

LLMs

RAG

Vector databases

Agent frameworks

ML Ops

LLM Ops

Responsible AI

AI governance

Education

MS in quantitative field

PhD in a relevant field

Tools

Git

Job description

Position Summary

Senior Manager, AI Engineering to advance enterprise AI capabilities through development, deployment, and governance of scalable AI/ML solutions, partnering with R&D, Commercial, and Corporate teams to turn data into actionable insights and business value.

Primary Responsibilities

• Execute enterprise AI strategy/roadmap; align AI/ML initiatives with business priorities and identify high-value use cases.
• Develop AI/ML solutions across R&D, Commercial, and Corporate functions.
• Deliver analytics and data science use cases for data-driven decision-making.
• Support AI governance with technical expertise on model risk, responsible AI practices, and lifecycle management.
• Assess feasibility, business value, and implementation risks for prioritization.
• Design, develop, validate, deploy, and support ML models and scalable ML/LLM pipelines using ML Ops/LLM Ops.
• Evaluate, implement, and optimize GenAI (LLMs, vector databases, endpoints, agent frameworks, guardrails) per enterprise architecture.
• Maintain model/prompt/dataset documentation for lineage, auditability, governance compliance, approvals, and system-of-record standards.
• Build reusable frameworks, reference implementations, and technical standards to improve scalability/efficiency.
• Partner with IT, Data Insights & Analytics, and InfoSec for AI platform support, infrastructure, vendor assessments, and RFI/RFPs.
• Apply governance standards (lifecycle management, bias/robustness testing, explainability, human oversight, incident response, regulatory compliance).
• Support compliance with GxP and evolving AI regulations (e.g., NIST AI RMF, EU AI Act readiness).
• Contribute to AI enablement (knowledge sharing, documentation, education, portfolio reviews).
• Monitor emerging AI/ML/regulatory developments; recommend architectures, platform strategy, and build-vs-buy.

Education/Experience/Skills (Required/Preferred)

• MS in quantitative field + 5+ years relevant experience; or PhD + 2+ years industry (or equivalent research) in ML/AI/data science.
• Production or research experience deploying/supporting ML/AI.
• Proficiency: Python, SQL; frameworks such as PyTorch/TensorFlow/scikit-learn.
• GenAI experience: LLMs, RAG, vector databases, agent frameworks.
• ML Ops/LLM Ops experience (version control, evaluation, monitoring, deployment).
• Knowledge of responsible AI, explainability, evaluation methods, and AI governance.
• Regulated/compliance-focused environment experience preferred.
• Domestic/international travel as needed.

Benefits/Compensation

• Discretionary bonus and equity awards eligibility.
• Salary range: $150,300—$187,900 USD.
• Competitive base/bonus/equity; medical/dental/vision; 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.