Machine Learning Engineer

Do you have experience in Software deployment?, * Strong hands-on technical expertise (not just theoretical knowledge).

• Experience working on systems deployed to production at scale.
• Ability to debug, take ownership, and proactively communicate issues.
• Open mindset to learn domain (email/IML), as domain expertise can be ramped up.
• Preference for mid-to-senior level candidates with real execution experience.
• experience working on production-scale systems
• debugging skills and proactive communication.
• Strong theoretical knowledge and practical fundamental
• task completion vs. problem-solving mindset

Expectations

• This role sits within core MLOps and ML infra systems that enable researchers and engineers. Focus is on ensuring scalable, efficient, and standardized ML workflows.
• Scalable ML workflows and pipelines, ML infra, efficient automation of ML workflows, robust data sampling / feature generation platforms, standardized training and deployment, GKE training and serving infra, knowledge distillation pipelines, foundational training tools
• Scalable ML workflows and pipelines, ML infra, efficient automation of ML workflows, robust data sampling / feature generation platforms, standardized training and deployment, GKE training and serving infra, knowledge distillation pipelines, foundational training tools
• Time to market, Training efficiency, infra uptime, cost optimization, pipeline reliability, onboarding speed
• Time to market, Faster experimentation cycles, reduced cost, stable deployments, high platform adoption
• 10+ years in large-scale ML systems
• Ownership-driven, pragmatic, fast-moving, strong collaborator with research + eng
• Reliability and consistency focus, scalability mindset, cost awareness, methodical approach

3.93.9 out of 5 stars Remote $90 - $95 an hour - Contract