Forward Deployed ML Engineer

Overview Rockstar is recruiting for a forward-deployed machine learning engineer role at a leading AI infrastructure company. The client is building the AI backbone for the next generation of intelligent products, helping fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings. Think of it as a full-stack backend for training, RL, inference, evaluation, and long-term model maintenance. Their customers are Series A-C AI companies building enterprise-grade products, and their promise is simple: they make AI systems better. The Role Remote, open globally. The company is hiring a Forward Deployed Machine Learning Engineer (FD-MLE) to work directly with customers to deploy, adapt, and operate production ML systems on top of its platform. This is a high-execution, high-ownership role. The engineer will be embedded in customer problems, shipping real models into real production environments—often under tight timelines and ambiguous requirements. If you enjoy being close to users, moving fast, and doing the unglamorous work required to make ML systems actually work, this role is for you. Why This Role Matters AI infrastructure often breaks down at the last mile—between a promising model and a reliable, scalable production system. As a Forward Deployed MLE, you are the connective tissue between the platform and customer success. You'll: Turn cutting-edge ML workflows into production-ready systems Feed real-world learnings back into product and platform design This role is ideal for early-career ML engineers who want maximum learning velocity, deep exposure to real systems, and accelerated responsibility. What You'll Do Customer-Facing Execution Deploy, fine-tune, and serve ML models in production environments (text, vision, embeddings, RL-adjacent workflows). 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. Systems & Infrastructure 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. Feedback & Iteration 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. Who You Are Required 1-3 years of production ML engineering experience You have deployed models that serve real users in production You've 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 This is an execution-heavy role where effort and follow-through matter You're comfortable putting in the hours when needed to get things working Clear communicator who can work directly with customers and internal teams Nice to Have Experience with LLMs, fine-tuning, embeddings, or RL-style workflows Exposure to GPU workloads, distributed training, or high-throughput inference Background in infra-heavy environments (ML platforms, data systems, dev tools) Interest in customer-facing or forward-deployed roles Why Join Globally remote with high trust and high autonomy Fast-moving, early-stage company with direct access to founders Outcomes > process Build infrastructure that powers the next generation of AI products Why This Isn’t a Fit (If applicable, remove or omit.) If not clearly a fit, consider replacing this with a brief note about the team culture or ideal candidate profile. #J-18808-Ljbffr