Bespoke Labs
AI Enterprise Engineer
About this role
Bespoke Labs seeks an AI Enterprise Engineer to partner with enterprise customers in developing and deploying specialized AI agents. You'll own the full lifecycle from understanding customer needs and defining agent specifications to benchmarking models, creating datasets, and optimizing performance through advanced techniques like fine-tuning and reinforcement learning.
What you'll do
- Partner directly with enterprise customers to define model and agent requirements and understand pain points
- Benchmark models and create datasets tailored to customer needs using Bespoke Labs tools
- Design and develop specialized agents with multi-turn capabilities
- Optimize model performance through context optimization, SFT, and RL techniques
- Analyze agent behavior, identify failure modes, and reward hacking issues
- Translate research insights into production-ready deployed agents
What they're looking for
- Python programming
- Machine learning and AI fundamentals
- Model evaluation, training, and fine-tuning
- Production ML deployment
- Reinforcement learning
- Agent behavior analysis
- Research intuition and problem-solving
- Customer collaboration and requirements gathering
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Bespoke Labs
Bespoke Labs builds specialized AI agents and reinforcement learning platforms that enable enterprises and researchers to develop, train, and deploy advanced AI systems at scale. The company is hiring AI engineers, product engineers, infrastructure engineers, full stack engineers, and research engineers to develop agent capabilities, build accessible RL platforms, and create production systems for complex agentic workflows.
View all jobs at Bespoke LabsLikely interview questions
- Walk us through your experience with model fine-tuning and post-training techniques like SFT and RL. What challenges did you encounter and how did you address them?
- Describe a time you deployed an ML model to production. What were the key considerations and how did you handle performance monitoring?