Forward Deployed Engineer, Post-Training
$270k - $300k • Remote • San Francisco
Posted 6h ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Categories
LLM Engineer
About the job
As a Forward Deployed Engineer (FDE) focused on Post-Training, you will be a hands-on technical partner to strategic customers who are using fine-tuning and reinforcement learning to build differentiated models on open-source foundations. You will work alongside Solutions Architects as the deep-domain specialist in post-training, guiding customers through data strategy, algorithm selection, experiment design, and evaluation, and sizing the compute required. Your role is crucial in ensuring the successful delivery of post-training Proofs of Concept (POCs), driving adoption of the fine-tuning and RL platform, and translating customer goals into measurable improvements in model quality. This position directly impacts customer success, company growth, and the enhancement of our core platform.
Responsibilities
- Guide customers in defining and executing their data strategy for model post-training, diagnosing data quality issues, and recommending data generation approaches.
- Recommend appropriate post-training algorithms for various use cases.
- Design well-controlled experiments and tracking methods, diagnosing training instabilities and reward hacking.
- Build evaluation frameworks with customers, including task-specific benchmarks and LLM-as-judge pipelines.
- Guide base model selection methodology and conduct baseline quality evaluations.
- Scope and size RL and fine-tuning workloads, collaborating with sales and capacity teams.
- Serve as the primary post-training technical point of contact for strategic accounts, leading technical execution from scoping to production.
- Shape the fine-tuning and RL product roadmap with field insights and drive early adoption of new features.
Requirements
- 5+ years in a technical role (ML engineering, Applied AI, FDE, or Solutions Architect) with recent hands-on experience in post-training LLMs.
- Expert-level, hands-on experience with fine-tuning and/or RL for LLMs (SFT, LoRA, DPO, GRPO, PPO).
- Strong track record in designing experiments, running ablations, and building evaluations.
- Proven ability to build and iterate on training datasets.
- Broad knowledge of state-of-the-art open-source models and judgment on base model selection.
- Strong Python skills and comfort working in production environments.
- Experience leading complex, customer-facing technical projects with exceptional project management and communication skills.
- Ability to work in a highly cross-functional environment.
Benefits
- Competitive compensation
- Startup equity
- Health insurance
- Flexibility in terms of remote work