Offer summary

Qualifications:

5+ years of software engineering experience with a focus on machine learning., Proven track record in developing and deploying large-scale ML models., Strong understanding of modern ML architectures and training techniques., Experience with model debugging, optimization, and performance tuning..

Key responsabilities:

Design and implement state-of-the-art ML models and training pipelines.

Develop efficient data and training strategies while applying novel machine learning techniques.

Lead model development with a focus on rapid experimentation and performance optimization.

Collaborate with the engineering team to optimize training infrastructure and deployment.

Job description

About Standard Bots

At Standard Bots, we're revolutionizing real-world automation by making robotic systems accessible to everyone. Our AI-powered platform enables robots to tackle unprecedented challenges through an intuitive instruction interface, bringing the power of software automation to physical spaces.

About the Role

We're seeking a Senior Machine Learning Engineer to develop and optimize our AI models and training systems. This is an exciting opportunity to apply your deep ML expertise to cutting-edge AI/robotics applications. You'll work closely with our engineering team to design, implement, and iterate on large-scale AI models while building efficient systems for rapid experimentation and deployment.

💡Experience with robotics isn’t required; we’re looking for experienced engineers ready to make a tangible impact in the AI robotics revolution. If you're excited about building scalable systems and working in the AI/robotics space, we’d love to hear from you.

In this role, you will:

Design and implement state of the art ML models and training pipelines:
- Apply novel machine learning techniques to wide range of robotics applications
- Develop efficient data and training strategies
- Implement model evaluation frameworks and metrics tracking
Lead model development and iteration with focus on:
- Rapid experimentation and prototyping of new model architectures
- Performance optimization and model debugging
- Transfer learning and fine-tuning strategies
Build robust evaluation and debugging systems to:
- Analyze model behavior and failure modes
- Implement interpretability tools and visualization frameworks
- Track and improve model metrics
Collaborate with engineering team to optimize training infrastructure and deployment