Make intelligence open and accessible to all.

What you’ll do

• Own technical strategy and end-to-end delivery of agentic systems, from customer discovery to production launch.
• Partner with Deployment Strategists and Sales to understand enterprise needs and architect transformative agentic applications.
• Build with state-of-the-art models, orchestrating LLM workflows and integrating with enterprise infrastructure.
• Collaborate with research teams to adapt and fine-tune models for customer-specific needs.
• Support deployments across hybrid environments, public cloud, VPC, and on-premises, for scale, performance, and reliability.
• Evolve the playbooks, processes, and best practices of a growing Forward Deployed Engineering org.

What they’re looking for

• Strong software engineering background shipping production systems in Python and TypeScript.
• Enterprise deployment experience in cloud or hybrid environments with Docker, Kubernetes, and CI/CD.
• Hands-on with modern AI stacks, vector databases, RAG pipelines, agent orchestration, evals, and fine-tuning.
• 3+ years delivering AI-driven enterprise solutions (FDE, SWE, or Applied AI Engineer).
• High agency and ownership in fast-paced, customer-facing environments where the playbook is still being written.

What you’ll do

• Lead post-training engagements with enterprise customers: assess their data, define training strategies, design reward signals and verifiers, prepare datasets, run training loops, and evaluate against customer-specific benchmarks.
• Design and build RL training environments for model adaptation, synthetic-data generation pipelines, reward-model training, and preference-data collection.
• Build evaluation infrastructure: define what “better” means for each use case, build eval harnesses, curate test sets, and establish baselines that measure real-world performance.
• Own the data pipeline from raw customer data to training-ready datasets, synthetic generation, quality inspection, cleaning, and format standardization.
• Deploy post-trained models across hybrid environments (public cloud, VPC, on-premises) for inference performance, cost efficiency, and reliability at scale.
• Shape and scale the post-training and evaluation practice, define playbooks and technical standards, and mentor engineers on the team.

What they’re looking for

• Hands-on LLM post-training at scale: built and operated RL training environments and preference-optimization workflows on 50B+ parameter models, shipped to production.
• Built synthetic-data generation pipelines, reward models, and verifiers, the data and feedback loops that make post-training work.
• Deep evaluation methodology: designing evals that measure what matters, reading training dynamics, and telling a benchmark-good model from one that actually works.
• Training infrastructure at scale: multi-node GPU clusters, large training runs, distributed-training debugging, and cost optimization.
• Strong SWE fundamentals, production-quality code, dataset/model versioning, and reproducible workflows.
• 6+ years engineering, including 2+ years leading LLM post-training (Tech Lead, Senior MLE on preference optimization, or Lead Applied Scientist running production RL).
• Customer-facing experience or genuine interest in developing it, translating domain requirements into training strategies and measurable outcomes.