The role

We seek an experienced Senior ML Solutions Architect to support customers leveraging Nebius Token Factory's serverless inference platform for open-source LLMs across multiple modalities. In this role, you will be collaborating with clients to design and implement customized LLM-based solution and architect scalable AI applications using our served models, and working together with our backend team to improve our platform to match the clients' needs.

You’re welcome to work remotely from Singapore.

Your responsibilities will include: 

• Design and implement LLM-based solutions using Nebius Token Factory’s inference services to drive business value and support customer goals
• Build production-ready applications leveraging our serverless LLM APIs, including multimodal models (text, vision, audio) and domain-specific models
• Provide technical expertise in prompt engineering, RAG architectures, model selection, and inference optimization
• Collaborate with product and engineering teams to surface customer feedback and shape the platform roadmap
• Guide customers in scaling from POC to production with a focus on performance, reliability, and cost efficiency

We expect you to have: 

• 5+ years of experience in ML/AI systems, with at least 2 years focused on LLMs and generative AI
• Deep knowledge of the LLM ecosystem, including model architectures and fine-tuning approaches
• Hands-on experience with:
  • Prompt engineering and LLM pipeline development, including evaluation
  • Agentic frameworks such as Langchain, Langsmith, smolagents, or equivalent
  • Vector databases and RAG implementation patterns
  • Deploying LLM-powered applications using APIs from OpenAI, Anthropic, or open-source models
• Strong Python programming skills
• Excellent communication skills, with the ability to clearly explain technical concepts to diverse audiences

It would be an added bonus if you have: 

• Experience with inference frameworks and libraries (e.g., vLLM, SGLang, TensorRT-LLM, Transformers)
• Familiarity with inference optimization techniques such as quantization, batching, caching, and routing
• Work with multimodal AI models (e.g., vision-language, speech)
• Proficiency with DevOps tools (Docker, Kubernetes)
• Contributions to open-source ML/AI projects

Preferred technical stack:

• Programming Languages – Python
• ML Frameworks and Libraries – vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI/Anthropic SDKs
• Frameworks for Agentic Pipelines – Langchain / Langsmith / smolagents / equivalent
• API and Web Frameworks – FastAPI, Flask
• MLOps and DevOps tools – Kubernetes (K8s), Docker, Git
• Cloud Platforms – AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)

Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

The Role

Your role, first and foremost, is to help our customers solve their most important problems. As a Software Engineer, this takes the form of building and deploying applications that utilize the power of our physics AI models to enable our customers to build better, faster, and cheaper.

Who We're Looking For

What a day might look like

Who We're Looking For

As a Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used.

You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products.

With at least 3 years industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready to not only execute but also lead and mentor others. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

This Role 

As a machine learning engineer, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.

You'll:

• Own the deployment of ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data, time‑series forecasting, anomaly detection, optimization & control) to customer production environments.
• Communicate results and trade‑offs to senior stakeholders; steer roadmaps and influence product direction with evidence.
• Lead scoping and architecture design for data/ML systems; define success metrics, delivery plans and quality bars.
• Excel at building robust and scalable ML systems, training and inference pipelines and APIs, running both on cloud and on-prem environments. The tech stack you will use for this includes: Python, PyTorch, Pandas, fastAPI, Scipy, Kubeflow, among others.
• Mentor and develop engineers and data scientists; provide technical direction and clear, calm decision‑making under pressure.
• Travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site.
• Own the scoping of new projects and work-streams with existing customers and taking part in bringing new customers to PhysicsX.

As an early member of the team, you’ll significantly influence our technical direction and will be involved in shaping future solutions and products, while developing your skills as a technical leader.

Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey! 

Who We're Looking For

As a Principal Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used.

You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products.

With at least 3 years industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready to not only execute but also lead and mentor others. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

This Role 

As a Principal MLE, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.

You'll:

• Own the deployment of ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data, time‑series forecasting, anomaly detection, optimization & control) to customer production environments.
• Communicate results and trade‑offs to senior stakeholders; steer roadmaps and influence product direction with evidence.
• Lead scoping and architecture design for data/ML systems; define success metrics, delivery plans and quality bars.
• Excel at building robust and scalable ML systems, training and inference pipelines and APIs, running both on cloud and on-prem environments. The tech stack you will use for this includes: Python, PyTorch, Pandas, fastAPI, Scipy, Kubeflow, among others.
• Mentor and develop engineers and data scientists; provide technical direction and clear, calm decision‑making under pressure.
• Travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site.
• Own the scoping of new projects and work-streams with existing customers and taking part in bringing new customers to PhysicsX.

As a senior member of the team, you’ll significantly influence our technical direction and will be involved in shaping future solutions and products, while developing your skills as a technical leader.

Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey! 

The Role

Your role, first and foremost, is to help our customers solve their most important problems. As a Senior Forward Deployed Software Engineer, this takes the form of building and deploying applications that utilize the power of our physics AI models to enable our customers to build better, faster, and cheaper.

Who We're Looking For

What a day might look like