Computer Science Jobs 2026 (Now Hiring) – Smart Auto Apply
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AI / ML Validation & Computer Software Assurance (Csa) Expert
$85 - $85 / hour
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Overview
Job Description
- Partner with Data Science, IT/Engineering, Quality, and Regulatory Affairs to embed validation and quality-by-design principles into AI development from inception (shift-left).
- Represent global validation among business and technical teams, attend sprint reviews, architecture discussions, and stage-gate decisions, not just formal quality checkpoints.
- Apply CSA's risk-based, critical-thinking approach to determine the right level of control for each AI/ML use case — ensuring teams move quickly on genuinely low-risk work while firmly enforcing rigor where patient safety, product quality, data integrity and/or corporate security is at stake.
- Coach project teams and their leadership on how to build validation and CSA thinking into their process from the start, so control isn't something bolted on afterwards.
- Translate control requirements into terms digital innovation teams will act upon, not just cite the regulation, but explain the risk in language that lands with engineers and product owners.
- Maintain a working inventory of AI/ML initiatives and current validation/control status, providing visibility to leadership.
- 10+ years of experience in computer system validation/CSV within a regulated life sciences environment, including demonstrated leadership applying Computer Software Assurance (CSA) principles — critical-thinking-based risk assessment, right-sized documentation, and defensible acceptance of risk
- Thought leadership and demonstrative expertise and in validating AI/ML systems together with strong working knowledge of AI/ML models, frameworks, and solutions, sufficient to engage credibly and in detail with data science and engineering teams, including:
- Model types (classical ML, deep learning, generative AI/LLMs, computer vision, NLP)
- Frameworks and tooling (e.g., TensorFlow, PyTorch, scikit-learn, MLOps/LLMOps platforms, cloud AI services)
- Training, fine-tuning, retrieval-augmented generation, prompt engineering, and model evaluation
- AI-specific risk considerations: bias, explainability, drift, hallucination, data lineage, adversarial robustness
- Deep familiarity with 21 CFR Part 11, Part 820/QMSR, GAMP 5 (2nd Edition), EU Annex 11, ICH Q9(R1), FDA guidance on AI/ML-based SaMD and CSA, and emerging AI regulatory frameworks (e.g., EU AI Act).
- Exceptional communication and stakeholder-management skills; comfortable being the person in the room whose job is occasionally to slow things down.
- Bachelor's degree in a relevant field (e.g., Computer Science, Engineering, Information Technology) preferred.
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