Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Perform aerodynamic and CFD analyses, build meshes and solver-ready cases, define simulation settings, automate workflows with Python, and validate numerical and physical results. Responsibilities include calculating lift and drag, evaluating convergence and mesh quality, diagnosing unstable or invalid simulations, selecting modeling assumptions, and documenting technical methodologies. The role supports an AI training project and requires strong fluid mechanics, aerodynamics, computational analysis, and programming expertise.

Design and evaluate rigorous hardware engineering problems for AI training projects. Develop reference solutions using calculations, simulations, Python scripts, and experimental reasoning. Model and validate hardware systems against physical test data, create verification methodologies, investigate failures, and document reproducible technical solutions. The role requires substantial hardware engineering ownership, strong programming ability, quantitative reasoning, and mandatory hands-on experience with AI Coding Agents.

Lead AI training project development based on real-world mechanical engineering workflows. Design multidisciplinary engineering scenarios, define requirements and acceptance criteria, decompose complex problems into technical tasks, coordinate engineering contributions, and evaluate design feasibility. Apply expertise in mechanical design, analysis, manufacturing, verification, testing, and root-cause analysis to ensure realistic professional standards and deliverables.

Evaluates and compares chatbot responses against business requirements and quality criteria. Gathers requirements, documents functional specifications, analyzes workflows and data, creates reports and dashboards, supports testing and UAT, tracks project risks and KPIs, and presents evidence-based findings. Collaborates with technical teams and stakeholders to improve AI output quality while maintaining evaluation consistency and meeting deadlines.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.