🕒 3 days ago
Lead the design and delivery of production machine learning systems, from data pipelines and model training to evaluation, inference, and deployment, while guiding engineers and improving model performance.
Staff Machine Learning Engineer
🕒 3 days ago
PythonPytorchJAXLarge Language Models
📜 Description
- Design and build ML systems spanning data processing, training, evaluation, inference, and deployment.
- Develop training and fine-tuning pipelines for large language models.
- Build evaluation frameworks to measure model quality, reliability, and performance.
- Optimise inference for latency, throughput, GPU utilisation, and cost.
- Develop data pipelines for collecting, processing, and validating training and evaluation datasets.
- Deploy, monitor, and troubleshoot production ML models and infrastructure; guide engineers through architecture decisions and reviews.
🛠️ Requirements
- 8+ years of software engineering or machine learning engineering experience, including production ML systems
- Strong Python programming skills and hands-on experience with PyTorch or JAX
- Experience training, fine-tuning and evaluating large machine learning models
- Experience building and operating GPU-based training or inference systems
- Strong understanding of distributed computing, data pipelines and ML infrastructure
- Experience optimising model performance, inference latency and resource utilisation
- Ability to design ML system architecture, review code and resolve complex production issues
- Experience providing technical leadership to ML engineers
- Experience with large language models, transformers and distributed model training
- Experience with inference frameworks such as vLLM or TensorRT-LLM
Full job description
About the Role
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
Role
Design and build ML systems covering data processing, model training, evaluation, inference and deployment
Develop training and fine-tuning pipelines for large language models
Build evaluation frameworks to measure model quality, reliability and performance
Optimise inference systems for latency, throughput, GPU utilisation and cost
Develop data pipelines for collecting, processing and validating training and evaluation datasets
Deploy, monitor and troubleshoot ML models and infrastructure in production
Make architecture decisions and guide engineers through technical design and code reviews
Work with research and application engineering teams to improve model performance in production
Requirements
8+ years of software engineering or machine learning engineering experience, including production ML systems
Strong Python programming skills and hands-on experience with PyTorch or JAX
Experience training, fine-tuning and evaluating large machine learning models
Experience building and operating GPU-based training or inference systems
Strong understanding of distributed computing, data pipelines and ML infrastructure
Experience optimising model performance, inference latency and resource utilisation
Ability to design ML system architecture, review code and resolve complex production issues
Experience providing technical leadership to ML engineers
Nice to Have
Experience with large language models, transformers and distributed model training
Experience with inference frameworks such as vLLM or TensorRT-LLM
Experience with synthetic data generation, data curation and model evaluation pipelines
Experience deploying ML systems for consumer applications
Similar jobs
Search more Machine Learning Engineer jobs🕒 3 days ago
Lead the design and delivery of production machine learning systems, from data pipelines and model training to evaluation, inference, and deployment, while guiding ML engineers and improving model performance.
🕒 3 days ago
Lead the design and delivery of production machine learning systems, from data pipelines and model training through evaluation, inference, deployment, and technical guidance for ML engineers.
🕒 3 days ago
Design and lead production machine learning systems end to end, from data pipelines and model training to evaluation, inference, and deployment, while guiding technical decisions and improving model performance.
Tech Lead, Machine Learning (Real-time AI)
🕒 7 days ago
Filevine👥 10,000+ employees🏢 Software Development🤝 B2B
🕒 7 days ago
Lead the technical direction of production machine learning systems for deposition and meeting products, building grounded LLM pipelines and evaluations while guiding architecture, reliability, and engineering practices.