We are hiring a senior engineering and software domain expert to work directly with a leading AI lab's research and program management teams, improving how frontier AI models reason about real engineering and software development work. Your engineering expertise is the substance of this role. You will review the quality of engineering knowledge work tasks, write the instruction specs and golden solutions that define what "correct" looks like, and build the benchmarks that show whether the model is genuinely improving. We are looking for a practicing specialist rather than a generalist. This is a full-time W-2 employment position with Cincinnatus LLC, with the opportunity to be placed at a leading AI lab as part of their extended workforce. You will be provisioned with client-issued accounts and equipment, and will work inside the client's own tools alongside their research teams. Location: This is a hybrid role based in the Bay Area, California. You must live in the Bay Area and work on-site with the client's team multiple days each week, when required. This is not a remote role. If you do not currently live in the Bay Area, you must be willing to relocate there at your own cost before the engagement starts. Relocation assistance is not provided. 2. Key Responsibilities Data QA and reviews: Vet the quality of engineering knowledge work tasks and model outputs, spotting missing behaviors, thin reasoning, code that runs but is subtly wrong, unhandled edge cases, and answers that read well but would not survive a real code review. Instruction specs and golden datasets: Write high-quality instruction specs, produce golden solutions to engineering problems, and define new engineering tasks that reflect how the work is actually done in practice. Benchmarks and domain depth: Design challenging engineering tasks and evaluation sets, and help build engineering-specific skills and tools together with the research team. Calibration: Work with client researchers and specialists in adjacent fields to keep standards consistent, translating tacit engineering judgment into explicit, teachable criteria. 3. Core Qualifications Education: Bachelor's degree or higher in computer science, software engineering, or a closely related engineering discipline. An MS or PhD is strongly preferred. Experience: 4+ years of substantive professional engineering practice, building and shipping production software or engineered systems at a reputable organization. Internships and academic coursework alone do not count. Domain depth: Genuine specialization in at least one area, for example distributed systems and backend infrastructure, security and cryptography, embedded and firmware, machine learning systems, developer tooling and platform engineering, or quality and test engineering. Seniority: Clear progression to a senior level, for example Senior, Staff, Principal, Architect, or Engineering Lead, with real ownership of systems and design decisions. Technical evidence: A visible record of engineering output such as shipped systems, granted patents, open source contributions, or published technical work. Preferred. AI fluency: Hands-on working use of large language models in your professional work, and the judgment to tell a well-reasoned answer from a plausible-sounding wrong one. Availability: Able to commit reliably to 40 hours per week for an initial engagement of 6 months. Location: Living in the Bay Area, California, and able to work on-site with the client's team multiple days each week, when required. Candidates not currently based in the Bay Area must be willing to relocate there at their own cost. Relocation assistance is not provided. Excellent written communication, and the ability to give precise, well-structured written feedback.

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