Software Engineer, Applied AI
Confidential Client is seeking a Software Engineer to develop and harden production AI systems that integrate agentic workflows with enterprise and government data under strict security and trust constraints. The role involves building scalable, reliable backend services and feedback mechanisms for AI model behavior, requiring strong software engineering skills and practical knowledge of ML and LLM fundamentals. This foundational position offers the opportunity to impact mission-critical AI infrastructure in high-stakes domains with a hybrid work arrangement in Seattle.
About CareerTakes
CareerTakes is a next-generation AI recruiting platform that connects early-career talent with real roles at established companies across regulated industries.
👉 Important disclosure: CareerTakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, a company in Software Development.
Applicants for this role may also receive access to additional matched opportunities through the CareerTakes platform.
About the Role
Our confidential client is building secure AI infrastructure for agentic workflows used in life-sciences innovation networks and intergovernmental emergency response. Backed by significant funding and grants, they partner with global, high-impact organizations on distributed workflows where reliability, security, and trust matter from day one. Their systems are already deployed in mission‑critical customer environments.
This is a foundational engineering role focused on building production AI systems where agentic workflows intersect enterprise data and trust boundaries. The role requires experience shipping AI systems used by real users and strong software judgment to harden systems for sensitive data, availability, and measurable quality.
What You’ll Do
- Build agentic workflows over enterprise and government data with clear rules for data access, tool calls, and human review/approval.
- Design context and grounding systems that provide models the right information at the right time without violating permissions or performance constraints.
- Work across backend services, APIs, async workers, data pipelines, internal tools, and product-facing surfaces.
- Build evals and feedback loops for model behavior and workflow outcomes to detect regressions and improve reliability.
- Turn domain-specific AI behavior into reusable product infrastructure instead of one-off customer logic.
- Move quickly from prototype to production-quality systems in collaboration with founders and engineers.
- Write clean, maintainable code and create clear abstractions.
- Use AI-assisted developer tools (e.g., Claude Code, Codex, ChatGPT, Cursor) responsibly; apply the same standards to generated code as to handwritten code.
Keywords: Applied AI, agentic workflows, LLMs, production ML systems, grounding, model evals, Python, TypeScript, React, gRPC, AWS, Kubernetes, Terraform, Snowflake, Docker, enterprise data security.
About You
- Track record of shipping production software and at least one AI product or workflow used by real users (enterprise or scaled consumer environments preferred).
- Strong software fundamentals; fluent in Python and/or TypeScript.
- Practical ML and LLM fundamentals — you understand model training, evaluation, serving, and deployment well enough to make sound engineering decisions.
- Experience or strong judgment across a modern stack (examples include React/TypeScript, Python services, gRPC, AWS, Kubernetes, Terraform, Snowflake, Docker).
- Preferable experience:
- Shipped production LLM or agentic workflows at an AI-native startup or applied-AI team.
- Built evals or feedback loops that caught real regressions.
- Debugged production failures in agent workflows (grounding, tool use, model/runtime boundaries).
- Built systems that operate over enterprise data with defined security boundaries.
- Worked in domains where incorrect outputs have serious consequences (scientific, medical, legal, financial, public sector).
- Owned meaningful product or platform surface area early in your career.
Details
- Compensation: $170,000 – $230,000 USD per year
- Location: Hybrid — Seattle, WA
- Employment type: Full-time
- Education: Bachelor’s degree (or equivalent experience)
- Visa: We do not sponsor visas for this role at this time. Candidates must be authorized to work in the U.S. for this position.
- Benefits: Company-paid health coverage (including dependents)
- Equity: Meaningful ownership for early engineers, with flexibility to extend for exceptional scope and impact
How to Apply & Hiring Notes
- CareerTakes will present qualified applicants to the confidential client; final hiring decisions are made by the confidential client.
- Applicants may be asked to complete technical interviews, take-home assignments, or work-sample evaluations relevant to production AI systems and software engineering.
- Reasonable accommodations are available for applicants with disabilities — please request accommodations during the process.
- Background checks or reference checks may be required as permitted by law.
Equal Opportunity & Hiring Transparency
CareerTakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.
If you have questions about how your data is used, please contact us directly.