Rockstar

Forward Deployed ML Engineer

Rockstar

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇨🇦 Canada

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Job Level

Mid-LevelSenior

Tech Stack

Cloud

About the role

  • 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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
ML modelsmodel architecturestraining loopsinference stacksdata pipelinesmodel trainingmodel servingdebuggingfine-tuninghigh-throughput inference
Soft skills
diligenthardworkingexecution-heavyclear communicatorcustomer-facingfollow-throughadaptabilityproblem-solvingcollaborationattention to detail