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Senior Machine Learning Engineer (Production ML & Research Collaboration)

Edison Smart® • United State
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Bridge the gap between research and production as a Senior ML Engineer at Edison Smart®, optimizing AI models for real-world robotics applications. Own the full lifecycle—from training to deployment—while collaborating closely with researchers to refine and scale models. Leverage PyTorch, cloud infrastructure, and distributed systems to enhance performance, cost-efficiency, and real-time decision-making in autonomous systems.

Key Highlights
Full ownership of ML model lifecycle: research-to-production transition, training, evaluation, deployment, and post-launch optimization
Collaborate with a small, high-impact team in a well-funded AI/robotics startup with real-world experimental applications
Focus on PyTorch, distributed training/inference, and LLM fine-tuning for high-performance, cost-efficient models
Key Responsibilities
Translate research ideas into production-ready, tested ML models and code
Design, build, and manage training and fine-tuning pipelines for PyTorch-based models
Develop evaluation frameworks to determine model readiness for deployment
Deploy optimized models while improving speed, cost-efficiency, and scalability
Monitor and refine model performance post-launch, iterating based on real-world feedback
Technical Skills Required
PyTorch Cloud and Container Technologies Python
Benefits & Perks
Up to $250,000 base salary
Equity compensation
Fully covered healthcare
Unlimited paid time off (PTO)
Nice to Have
Experience with LLM fine-tuning
Model serving or agent systems expertise
Scientific ML or reinforcement learning knowledge

Job Description

Machine Learning Engineer

New York, NY (on-site) | Up to $240k base + equity

I'm working with a well funded AI and robotics startup in NYC. Their robots run real world experiments around the clock, and AI decides what to try next. Small team, serious backing, and models that get used for real.

They need an ML Engineer to take models from research into production, working side by side with their researchers and owning everything from training to serving.

What you'll do

  • Turn research ideas into working, tested code
  • Build and run training and fine tuning pipelines
  • Build the evals that decide if a model is ready to ship
  • Deploy models and improve speed and cost
  • Own how models perform after launch
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