Project description
About the Project: Over 5 billion people use traditional, non-AI-native apps daily for email, notes, tasks, and calendars. Our client's mission is to build proactive applications for everyday users who aren't accustomed to complex prompting. Their platform aims to bring intelligence to conversations, errands, organization, and workflows effortlessly. By focusing on persistent context, real-world task execution, and high reliability for long-running workflows, their app minimizes AI hallucinations—with the ultimate objective of organizing users' lives so they can focus on what truly matters.
Technical requirements
- Python
- PyTorch / JAX
- LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
- Cloud infrastructure
- Distributed systems
- ML/data pipelines and workflow orchestration
- GPU infrastructure and performance tooling
- Vector databases and retrieval infrastructure
Responsibilities
As an ML Platform Engineer, you will build the infrastructure and systems that power our client's AI capabilities. You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement. You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.
- Build and operate the ML infrastructure and platforms powering the AI products.
- Design systems for model training, evaluation, deployment, inference, and experimentation.
- Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads.
- Improve reliability, scalability, latency, throughput, and cost efficiency of AI systems.
- Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement.
- Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster.
- Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions.
- Build production observability, monitoring, tracing, and alerting for AI/ML workloads.
- Identify bottlenecks across the ML stack and continuously improve system performance.
- Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure.
Must have
- Strong software engineering fundamentals and experience building production systems.
- Experience building ML infrastructure, platforms, or production machine learning systems.
- Experience with model deployment, inference, evaluation, or data pipelines.
- Strong understanding of distributed systems and system reliability.
- Ability to write clean, maintainable, production-quality code.
- Comfortable working in ambiguous, fast-moving environments.
- Bias toward ownership, experimentation, and continuous improvement.
Recruitment process
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. Please note that due to the volume of applications, we will only contact selected candidates.
Got questions?
To learn more details about this job contact Joanna at [email protected]