Forward Deployed ML Engineer

1-3 years of production ML engineering experience. Deployed models that serve real users in production. Worked on training, inference, or ML systems end-to-end. Strong fundamentals in ML engineering: data pipelines, model training, evaluation, and serving. Comfortable writing production-quality code and debugging complex systems. Extremely diligent and hardworking, comfortable putting in the hours when needed. Clear communicator who can work directly with customers and internal teams. Experience with LLMs, fine-tuning, embeddings, or RL-style workflows (Nice to Have). Exposure to GPU workloads, distributed training, or high-throughput inference (Nice to Have). Background in infra-heavy environments (ML platforms, data systems, dev tools) (Nice to Have). Interest in customer-facing or forward-deployed roles (Nice to Have).

Deploy, fine-tune, and serve ML models in production environments. Work hands-on with customer data, model architectures, training loops, and inference stacks. Debug performance issues across training, evaluation, latency, cost, and reliability. Adapt the platform to customer-specific workflows and constraints. Build and maintain model-serving pipelines (batch and real-time). Optimize inference performance (throughput, latency, cost). Help productionize evaluation, monitoring, and retraining workflows. Work across cloud infrastructure, GPUs, and ML tooling stacks. Act as the “voice of the customer” to internal product and engineering teams. Identify recurring patterns, edge cases, and gaps in the platform. Contribute to internal tooling, templates, and best practices.