The opportunity
What you will own
- Develop model orchestration, retrieval, inference and evaluation pipelines.
- Create structured-output, guardrail and observability mechanisms for production AI services.
- Design secure data pipelines and minimise unnecessary exposure of sensitive information to AI systems.
- Benchmark models against accuracy, latency, safety, cost and product-specific quality criteria.
- Collaborate with product and security teams on misuse resistance, prompt-injection defence and AI threat modelling.
- Build repeatable evaluation datasets and deployment controls for model changes.
What you bring
Core qualifications
- Strong Python and production machine-learning engineering experience.
- Experience with modern LLM, retrieval, evaluation or deep-learning systems.
- Demonstrated ability to move AI prototypes into observable, maintainable production services.
- Knowledge of data privacy, model-security and AI safety risks.
- Strong quantitative and experimental reasoning.
Additional strengths
Preferred experience
- Experience with healthcare AI, developer tooling, secure code analysis or agentic systems.
- Experience with ML platform/MLOps tooling and GPU/cloud inference.
- Experience designing adversarial evaluations or AI red-team tests.
Employment, compensation and fair recruitment
The published USD range is a U.S.-market reference range for this role. Final compensation, benefits, employment classification, payroll, tax treatment and statutory entitlements are determined by the employing entity, work location, experience and applicable law.
CloudTen does not charge candidates recruitment, placement, processing or employment fees. Qualified applicants are assessed against job-related requirements. Reasonable accommodation is available during recruitment where required by applicable law.
BYU-Pathway Worldwide students and graduates: qualified candidates are especially welcome to apply. Participation in recognised CloudTen talent-development or university programmes may receive positive consideration where lawful, while appointments remain merit-based and subject to the published requirements.
