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Machine Learning Operations Engineer

The Machine Learning Operations Engineer supports our machine learning infrastructure by ensuring seamless model training, optimization, and deployment. This role is perfect for a tech-savvy individual who enjoys managing machine learning systems and hardware configurations rather than focusing solely on programming, although coding experience would be a strong plus. The ideal candidate is a computer enthusiast with a knack for machine learning infrastructure and model optimization with a passion for working in a collaborative, fast-paced environment.


Responsibilities
  • Maintain and manage the software configuration of on-premises machine learning hardware to support optimal performance for training neural networks.  
  • Set up and maintain cloud-based training environments, primarily on Google Cloud Platform, to facilitate model experimentation and scalability.  
  • Automate training workflows to drive continuous improvement of vision models, reducing manual overhead and enhancing efficiency.  
  • Develop automated accuracy assessments and generate reports to evaluate and compare the performance of newly trained neural networks against existing models.  
  • Ensure predictable and efficient turnaround times for training models with updated datasets to meet project timelines.  
  • Organize and manage model weights and associated documentation in various formats for deployment across on-premises, cloud, and edge environments.  
  • Apply quantization and pruning techniques to models to enhance computational efficiency without sacrificing accuracy.  
  • Design and deploy infrastructure for low-latency inference to enable real-time performance for large-scale models (e.g., vLLMs).


Requirements
  • Proven experience with Linux server maintenance, including both on-premises and cloud environments.  
  • Proficient in scripting with Bash and Python to streamline system and model management.  
  • Hands-on experience with neural network training, data loaders, and data pre-processing pipelines.  
  • Familiar with data and model parallelism strategies for improving training speed and efficiency.  
  • Knowledgeable in neural network model conversion and optimization for deployment on diverse hardware.


Preferred Qualifications
  • Familiarity with Google Cloud Platform for machine learning operations.  
  • Experience with specialized hardware platforms such as Nvidia Jetson, Triton Inference Server, and NIM.  
  • Skilled in OpenVINO and ONNX for model conversion and optimization.  
  • Experience training or fine-tuning large language models (LLMs) would be a significant advantage.  
  • Programming experience in Python and C++ is beneficial but not mandatory.  
  • Strong written and verbal communication skills for documentation and collaboration.  
  • Passion for machine learning technology and an aptitude for problem-solving in fast-paced environments.


$90,000 - $150,000 a year
The base salary offered is based on market location, and may vary further depending on individualized factors for job candidates, such as job-related knowledge, skills, experience, and other objective business considerations. Subject to those same considerations, the total compensation package for this position may also include other elements, including equity compensation, in addition to a full range of medical, financial, and/or other benefits. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

At Simbe, you will be at the forefront of retail innovation, working with cutting-edge AI and robotics technologies to transform retail operations. Our culture is dynamic, inclusive, and driven by a passion for improving the way retailers operate and serve their customers. Join us to be a part of a team that is not only reshaping the future of retail but also offering immense value to our clients worldwide.


Simbe Values: R. E. T. A. I. L.

Result Driven - We are customer-centric and results-driven. We strive to create immense value for our team, partners, customers, and investors. 

Empathetic - We are sensitive and mindful. We support each other in challenging times, both professionally and personally.

Transparent - We highly value open communication internally, and with our partners and customers. We are receptive to feedback.

Agile - We are agile and always eager to learn. We quickly adapt to changes and customer needs.

Innovative - We are bold and innovative, with an intense focus on product design and user experience.

Leaders - We strive for excellence. We are accountable, the best at what we do, and leaders in our field.

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What You Should Know About Machine Learning Operations Engineer, Simbe Robotics

As a Machine Learning Operations Engineer at Simbe, you'll play a critical role in supporting our advanced machine learning infrastructure, ensuring seamless model training, optimization, and deployment. If you thrive in a tech-savvy, collaborative environment and enjoy diving into machine learning systems rather than solely coding, this position is perfect for you! Your expertise will shine as you maintain and manage our on-premises hardware for optimal performance while setting up and maintaining cloud-based training environments, primarily on Google Cloud Platform. You will automate training workflows and enhance efficiency by driving continuous improvement of our vision models. Additionally, you will develop automated accuracy assessments, generate insightful reports, and ensure that we meet project timelines with predictable turnaround times for training models. As you organize and manage model weights and documentation, you will also employ quantization and pruning techniques to boost computational efficiency without compromising accuracy. Bring your passion for machine learning technology to the forefront at Simbe, and help us revolutionize the retail landscape! We're excited to have someone like you join our dynamic and inclusive team, where innovation is a core value and your contributions directly impact our client's success.

Frequently Asked Questions (FAQs) for Machine Learning Operations Engineer Role at Simbe Robotics
What are the main responsibilities of a Machine Learning Operations Engineer at Simbe?

The Machine Learning Operations Engineer at Simbe is responsible for maintaining the software configuration of both on-premises and cloud-based machine learning hardware to ensure optimal performance for training neural networks. Key tasks include setting up cloud environments on Google Cloud Platform, automating training workflows, developing accuracy assessments, and organizing model weights for various deployment environments.

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What qualifications do I need to apply for the Machine Learning Operations Engineer position at Simbe?

To apply for the Machine Learning Operations Engineer position at Simbe, you should have proven experience with Linux server maintenance and cloud environments, along with scripting skills in Bash and Python. Hands-on experience in neural network training, familiarity with data parallelism strategies, and knowledge of model conversion for diverse hardware are also important qualifications.

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Is prior experience with Google Cloud Platform required for the Machine Learning Operations Engineer role at Simbe?

While familiarity with Google Cloud Platform is preferred for the Machine Learning Operations Engineer role at Simbe, it's not strictly required. However, having experience with specialized hardware platforms, such as Nvidia Jetson or Triton Inference Server, will be an added advantage in this position.

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How does Simbe support employee development for Machine Learning Operations Engineers?

Simbe fosters a culture of continuous learning and support, encouraging Machine Learning Operations Engineers to expand their knowledge in machine learning technologies. The company promotes professional development through collaboration, transparency, and innovative projects that allow team members to hone their skills and grow within the organization.

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What is the salary range for a Machine Learning Operations Engineer at Simbe?

The salary range for a Machine Learning Operations Engineer at Simbe is between $90,000 and $150,000 per year. The actual salary offered may vary based on market location, individual qualifications, skills, and experience, as well as additional benefits provided in the total compensation package.

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Common Interview Questions for Machine Learning Operations Engineer
Can you describe your experience with maintaining Linux servers in cloud environments?

Discuss specific projects where you've managed Linux servers, focusing on tasks such as updates, security measures, and performance optimization. Highlight any cloud platforms you've worked with and any challenges you faced, along with how you overcame them.

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What scripting languages and tools do you use for automating machine learning workflows?

Mention your proficiency in scripting languages like Bash and Python and provide examples of how you've used them to automate processes within training environments. Describe any specific frameworks or libraries you utilized, such as TensorFlow or PyTorch.

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How do you approach the optimization of neural network models?

Talk about strategies you've implemented to optimize models, including quantization and pruning techniques. Discuss how you've measured performance improvements and any specific algorithms or tools you used for optimization.

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What steps do you take when setting up a cloud-based training environment?

Outline the process you follow to establish cloud-based training, including resource allocation, environment configuration, and deployment of necessary software. Emphasize your experience using Google Cloud Platform or similar services.

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Can you explain a challenging problem you faced in model training and how you solved it?

Provide a clear example of a challenging issue you encountered, such as data bottlenecks or resource constraints. Discuss the steps you took to identify the issue and the strategies you employed to resolve it effectively.

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What is your experience with data loaders and preprocessing in machine learning?

Share your familiarity with data loaders and the types of preprocessing steps you’ve implemented. Highlight the libraries you’ve used, such as NumPy or Pandas, and any techniques that improved model input data handling.

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How do you ensure the accuracy of your machine learning models?

Discuss methods you employ for validating model accuracy, such as cross-validation, confusion matrices, and error analysis. Describe any automated accuracy assessment tools or scripts you've developed for this purpose.

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What experience do you have with deploying models in production environments?

Detail your experience in deploying machine learning models, focusing on the processes and tools involved. Mention how you’ve handled monitoring, versioning, and updates post-deployment to ensure model performance remains optimal.

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Can you provide an example of using data and model parallelism to speed up training?

Describe a scenario where you implemented data or model parallelism, including the setup needed and the results achieved. Explain the benefits this approach brought to the training speed and efficiency.

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How do you stay updated on the latest machine learning trends and technologies?

Talk about your methods for keeping current with machine learning advancements, such as following industry blogs, participating in online forums, or attending conferences. Highlight specific resources or communities that you find valuable.

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Full-time, remote
DATE POSTED
January 3, 2025

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