Software Engineer – Model Evaluation, Benchmarking

Job not on LinkedIn

🕒 February 25

🏢🏡 San Francisco – Hybrid

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧑‍💻 Full-stack Engineer

👻 Ghost score 67%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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SPREEAI

WebsiteLinkedInAll Job Openings

11 - 50 employees

👗 Fashion

🤖 Artificial Intelligence

🛍️ eCommerce

💰 Funding Round - SpreeAI on 2025-05

Fashion • Artificial Intelligence • eCommerce

SPREEAI is an AI-driven fashion technology company that provides real-time virtual try-on experiences for apparel, embedded directly into a brand's shopping platform. Customers can snap or upload a photo or select a model to see garments styled and fitted instantly, helping brands increase personalization and conversion. SPREEAI partners with fashion labels, industry organizations, and academic institutions to advance its fit and visualization technology within e-commerce.

📋 Description

• Build automated evaluation pipelines for multimodal AI models. • Benchmark diffusion models, vision systems, and generative workflows. • Validate model checkpoints and detect regressions across versions. • Develop evaluation metrics for realism, consistency, and performance. • Integrate evaluation tooling into CI/CD workflows. • Collaborate with ML researchers and infrastructure teams to ensure production readiness. • Analyze failure modes and propose evaluation strategies.

🎯 Requirements

• Degree in Computer Science, AI, Engineering, or comparable combination of education and practical experience. • Strong programming skills in Python. • Familiarity with object-oriented programming (C++, Java, Python, or similar). • Strong data structures and algorithms fundamentals. • Understanding of machine learning experimentation workflows. • Experience evaluating vision or generative models. • Familiarity with HuggingFace ecosystem or open-source ML toolkits. • Experience building automated test frameworks or benchmarking tools. • Knowledge of diffusion models or multimodal architectures. • Experience with data analysis tools (NumPy, Pandas, visualization libraries).

🏖️ Benefits

• Health insurance • Professional development opportunities • Flexible work arrangements