Machine Learning Engineer (Platform)

As a Machine Learning Engineer at Artera, your primary responsibilities will revolve around building tools and libraries that facilitate the development and deployment of AI products. You’ll optimize GPU and CPU efficiencies, manage data throughput for large-scale models, and enhance our observability infrastructure to monitor and optimize performance.

To qualify for the Machine Learning Engineer role at Artera, candidates should have 4+ years of software engineering experience, along with 3+ years leveraging frameworks like PyTorch, TensorFlow, or JAX. Additionally, proficiency in AWS, Docker, and Kubernetes is required for managing our machine learning infrastructure.

Candidates for the Machine Learning Engineer at Artera should be familiar with technologies such as PyTorch, TensorFlow, AWS, Docker, and Kubernetes, along with optimization tools like TorchScript and ONNXRuntime. Experience with ML orchestration frameworks such as Ray, Kubeflow, and MLFlow will also be beneficial.

The salary range for a Machine Learning Engineer at Artera is between $140,000 and $180,000 annually. This base salary is competitive and will be tailored to your experience and qualifications, with additional equity compensation as a core part of our offering.

Yes, Artera supports flexible working arrangements for Machine Learning Engineers as this role is remote within the US. We value work-life balance and also offer unlimited paid time off (PTO) to support our employees’ personal needs.

When answering this question, focus on your hands-on projects utilizing PyTorch or TensorFlow. Discuss specific models you've built, optimizations you've implemented, and the impact those had on performance. Highlight any unique challenges you overcame and the results of your work.

Outline a structured approach, including data preprocessing, model selection, and hyperparameter tuning. Talk about using ML frameworks and tools to streamline pipelines, particularly focusing on how you ensure efficiency in data handling and model deployment.

Explain your experience with data storage systems, processing frameworks, and any distributed computing tools you've employed. Emphasize your understanding of data throughput and reliability in large-scale environments while giving examples of specific technologies you've used.

Discuss your practical experience with AWS, mentioning specific services you’ve used (like S3, EC2, or Lambda) related to machine learning. Provide examples of how you've optimized costs or performance through your understanding of cloud architectures.

Explain that observability provides insights into system performance, allowing for quicker identification and resolution of issues. You can also mention tools used to monitor model performance and how you’ve implemented changes based on observed data.

Share a specific example that outlines the project's goals, the challenges faced, and the solutions implemented. Be sure to include any teamwork or collaboration aspects that contributed to the project’s success.

Discuss how GPUs accelerate model training by providing the necessary computational resources for handling complex computations simultaneously, emphasizing their importance for large datasets and deep learning models.

Talk about maintaining open communication and understanding the model developers' needs. Share any practices that facilitate collaboration, such as regular check-ins or shared tooling, that enhance teamwork and outcome quality.

Discuss methodologies you've used to architect scalable solutions, including load balancing, efficient data management, and performance benchmarking strategies to ensure your models can handle increasing workloads.

Describe your experience with deployment processes, including any frameworks or tools (like Docker or Kubernetes) you’ve used. Highlight best practices you've adhered to ensure stability and performance post-deployment.