Forward Deployed ML Engineer
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Job Description
Deploy machine learning systems for customers, engaging directly to adapt and operate production systems globally. Join the team building AI's future across diverse applications in a remote setting.
Responsibilities:
- 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.
- 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.
Requirements:
- 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.
- 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.
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