Enterprise AI Post-Training Lead

Bespoke Labs; United States; Hybrid; USD 300,000 - 350,000; Full time

Benefits offered by this job

Health/dental/vision coverage

401(k) plan

Daily onsite lunch

Visa sponsorship & relocation support

Direct industry impact

Job summary

Bespoke Labs is an AI research lab focused on data and RL environment curation for training and evaluating agents. This role centers on standing up our enterprise post-training capability and delivering production-ready models to paying customers.

You will curate data, build evaluation suites, fine-tune models, and prove their value to enterprise stakeholders. Ownership and direct collaboration with customers are essential.

Qualifications

• Shipped at least one LLM deployed to production with measurable success.
• End-to-end eval ownership with benchmarks and clear metrics.
• Understanding of regression risks when updating models.
• Customer-facing experience translating requirements into model behavior.
• Strong fundamentals in ML and software for production deployment.
• Ownership of full post-training lifecycle from data to deployment.

Responsibilities

• Ship enterprise-grade models post-training and fine-tune for production.
• Define evaluation suites and calibrate judges against experts.
• Curate and filter high-impact datasets with strict standards.
• Identify and mitigate regression risks during updates.
• Engage with stakeholders to translate requirements into metrics.
• Deploy with cost and privacy considerations for enterprise latency.
• Lead frontier tools and workflows for quality and speed.

Skills

Shipped models

Eval ownership

Regression risk understanding

Customer-facing

Production deployment

Ownership mindset

Tools

Llama

Qwen

Mistral

DeepSeek

Job description

Bespoke Labs is an AI research lab focused on data and RL environment curation for training and evaluating agents. This role centers on standing up our enterprise post-training capability and delivering production-ready models to paying customers.

You will curate data, build evaluation suites, fine-tune models, and prove their value to enterprise stakeholders. Ownership and direct collaboration with customers are essential.