Applied AI Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Hybrid From the posting source checked Jun 20, 2026

In-office days

2 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Applied AI Engineer San Francisco | Hybrid | Remote ABOUT ARTOS: At Artos, we build tools that help biopharma companies create and manage their R&D documentation in a fraction of the time. If you're looking to join a team whose mission is to fundamentally change the way that drug development gets done, we'd love to talk to you. About the Role: We're growing fast, and we're looking for an engineer who thrives in a high-velocity environment and wants to do meaningful work. At Artos, you'll help accelerate development of a platform that supports companies - from innovative biotech startups to the world's largest pharmaceutical firms - in delivering life-saving treatments to patients faster than ever before. As a core member of Artos's engineering team, you'll play a critical role in developing, scaling, and expanding the Artos platform to serve regulatory needs for pharma and life science companies around the globe. Qualifications: - Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience) - 2+ years of software development experience building and deploying AI/ML applications - Hands-on experience building LLM-based applications - Designing multi-step LLM workflows and task-specific agents - Experience working with frontier models (e.g., OpenAI, Anthropic, Google) - Experience with AI tools as a user, specifically AI code editors - Developing advanced prompt engineering strategies, evaluation frameworks, and RAG pipelines - Conducting technical R&D to explore and define the boundaries of model functionality - Use of evaluation tools such as Langfuse or LangSmith

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