Remote Software engineering Jobs · PyTorch

• Design, build, and productionize ML models for fine-tuned, Retrieval-Augmented Generation (RAG), and generative AI features.
• Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.
• Collaborate with product and engineering to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.

Participate in the design, maintenance, and improvement of Torc's AA frameworks. Work with cloud tools like Terraform, AWS Managed Services, and EKS. Build solutions for AA that have reached the production stage.

• Lead development of models for autonomous vehicles to perceive and interpret road geometry.
• Define next-generation deep learning architectures for extracting road and lane semantics from sensor data.
• Develop evaluation frameworks for lane and road geometry accuracy and cross-domain generalization.

Design, develop, and optimize ML models for localization, including learned pose estimation, map-matching, and sensor fusion using camera, LiDAR, and radar data. Collaborate with robotics and mapping teams to integrate localization models into real-time autonomy stacks with strict performance requirements. Contribute to system design, documentation, best practices, and code reviews across ML and autonomy teams.

• Build and design end-to-end training pipelines for AI models, covering data ingestion to inference.
• Architect scalable inference systems using tools like vLLM, TensorRT-LLM, or DeepSpeed.
• Shape early product direction by experimenting with new use cases and building AI-powered experiences.

• Design, build, and deploy machine learning models for personalization, automation, and insights.
• Manage the full ML lifecycle: data preprocessing, feature engineering, training, tuning, evaluation, deployment.
• Integrate ML outputs into user-facing products and backend systems.

• Design, build, and ship production-grade ML models for high-impact use cases.
• Lead model architecture decisions, data pipeline design, and deployment strategies.
• Collaborate across product, engineering, and data teams to drive AI initiatives end-to-end.

• Build and optimize machine learning models analyzing security data, extracting actionable insights, and identifying trends.
• Deploy and maintain containerized workloads supporting machine learning development, deployment, and monitoring.
• Collaborate with cross-functional teams to design data pipelines efficiently processing petabytes of raw internet security data.

As an Associate Machine Learning Engineer, you will help develop AI and ML systems that improve how Spotify detects, reviews, and manages risky or non-compliant activity. You’ll work with engineers, data scientists, and policy partners to design and evaluate models, automate performance tracking, and enhance transparency and auditability. This is a hands-on, collaborative role where you’ll learn from experienced practitioners while building solutions that advance safety, compliance, and responsible innovation.

• Research and develop computer vision and deep learning models.
• Build and maintain training and inference pipelines using frameworks like PyTorch or TensorFlow.
• Evaluate model performance using real-world data and optimize for accuracy, speed, and scalability.