Senior Manager, Forward-Deployed AI Evaluation

Socket.dev; New York (NY); Hybrid; USD 185,000 - 321,600; Full time

Job summary

Snorkel AI is seeking a Senior Manager on the Forward Deployed Research team to own how we show what our data does. You’ll lead benchmarking frontier models against our data, tune customer and open-source models, and turn results into evidence behind our data pitch.

This player‑coach role covers system ownership, tooling, hiring a small team, and collaboration across GTM, Research, and Engineering to build scalable evaluation platforms.

Qualifications

• 8+ years in applied ML, model evaluation, or research‑intensive engineering.
• Experience building data or evaluation pipelines and scalable tooling.
• Deep knowledge of model evaluation, benchmarks, metrics.
• Fluent with frontier AI concepts (LLMs, RL techniques) and related domains.
• Proven track record leading technical teams.
• Ability to translate results into insights for technical and GTM audiences.
• Comfort in fast-moving environments with ambiguity.

Responsibilities

• Define how we benchmark and tune models on our data.
• Recruit, hire, and develop a small team of engineers and researchers.
• Own methodology, playbooks, and quality bar for evaluation.
• Turn benchmark results into actionable insights for accounts and data series.
• Turn data lift into presales material and intelligence.
• Partner across GTM, Research, and Engineering.
• Serve as the technical authority on evaluation and credibility.

Skills

Applied ML

Model evaluation

Data pipelines

Software engineering

Frontier AI

Team leadership

Technical storytelling

Ambiguity tolerance

Education

MS in CS / ML / related field

Job description

Snorkel AI is seeking a Senior Manager on the Forward Deployed Research team to own how we show what our data does. You’ll lead benchmarking frontier models against our data, tune customer and open-source models, and turn results into evidence behind our data pitch.

This player‑coach role covers system ownership, tooling, hiring a small team, and collaboration across GTM, Research, and Engineering to build scalable evaluation platforms.