RISC AI Evaluation freelance Science & Research
We’re looking for an experienced chip design engineer to evaluate Mistral models on real-world semiconductor design workflows. You’ll combine hands-on engineering expertise with benchmark development and technical review to improve how AI models perform tasks across RTL development, synthesis, physical design, and tapeout.
What You’ll Do
- Evaluate AI model performance: Assess recent Mistral models on chip design tasks, identifying errors, limitations, and opportunities to improve technical accuracy and practical usefulness.
- Develop chip design benchmarks: Scope realistic tasks with clear requirements, appropriate difficulty levels, reference solutions, and measurable evaluation criteria.
- Review vendor-delivered tasks: Validate tasks involving RTL coding, logic synthesis, and synthesized netlists for correctness, completeness, reproducibility, and relevance to engineering workflows.
- Guide synthetic task generation: Provide domain expertise across the design flow, from RTL coding and verification through physical design, timing closure, and tapeout.
- Create high-quality seed tasks: Develop original chip design problems, supporting artifacts, and reference solutions to seed synthetic task generation.
- Define quality standards: Establish review criteria that account for functional correctness, synthesizability, timing constraints, and power, performance, and area.
- Collaborate with AI and data teams: Translate engineering findings into actionable feedback that improves model evaluations and generated training data.
What You Bring
- Significant hands-on experience in ASIC or SoC design, with deep expertise in RTL development and synthesis.
- Strong proficiency in Verilog or SystemVerilog, including writing, debugging, and reviewing synthesizable RTL.
- Experience with EDA tools for simulation, synthesis, and implementation, including interpreting reports and diagnosing design issues.
- Working knowledge of the RTL-to-GDSII flow, including static timing analysis, physical design, timing closure, and signoff.
- Ability to create self-contained engineering tasks with clear specifications, testbenches or validation methods, and technically sound reference solutions.
- Strong technical judgment and attention to detail when reviewing code, netlists, constraints, and design artifacts.
- Clear written communication and the ability to explain complex design concepts to AI researchers, engineers, and data teams.
Preferred Qualifications
- Experience contributing to a chip design through tapeout.
- Experience evaluating AI-generated code or developing technical benchmarks, datasets, or automated validation workflows.
- Proficiency in Python, Tcl, or shell scripting for EDA automation and results analysis.
- Familiarity with formal verification, equivalence checking, or open-source chip design tools.
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- 19 Oct 2026 Braintrust first sighting
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