Senior ML Engineer (AI Research/ Portability)

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role This role is for Nebius AI R&D, a team focused on applied research in AI. Our Portability research aims to make intelligent agent systems work reliably as models, providers, harnesses, skills, memory systems, and deployment environments change. We build and evaluate portable layers that preserve capability, context, identity, provenance, and user control across heterogeneous systems. Research areas include: Per-turn model routing across quality, cost, latency, capability, cache state, and reliability objectives Provider and protocol portability across frontier models, open-source models, local inference, and compatible APIs Agent and harness interoperability, including transferable skills, capability profiles, actions, tools, and trajectories Portable, user-owned memory and context with scoped identity, provenance, retrieval, feedback, and reviewable compaction Agent interchange standards, conformance testing, tool and MCP access, and agent-to-agent communication Agent and harness optimization through evaluation, distillation, customization, and multi-agent learning You will design and build research prototypes and robust systems at the seams between models, providers, and agent runtimes. You will formulate research questions, develop evaluation methods, test ideas in realistic agent workflows, and turn promising results into reusable components. The work will often involve collaboration with adjacent research, infrastructure, security, product, and engineering teams, where findings are validated and applied in practice. We are currently looking for senior- and staff-level ML engineers to work on research in areas such as: Learned, rule-based, and hybrid model routing, cascading, and candidate-ranking systems Quality-cost-latency trade-offs, uncertainty estimation, exploration, and outcome-aware routing Multi-provider gateways, protocol translation, catalog normalization, and fail-closed execution contracts Portable agent skills, harness capability discovery, package adaptation, and cross-harness conformance Memory, identity, context, trajectory, and outcome representations that remain portable across agents and models Retrieval, context selection, context compaction, and feedback systems with explicit provenance and trust boundaries Agent interoperability standards, including metadata, action formats, plugins, tools, MCP , and agent-to-agent interfaces Agent optimization, teacher-student distillation, skill generation, harness customization, and multi-agent learning Benchmarking and evaluation infrastructure for model, router, memory, skill, and harness changes Some examples of what your responsibilities might include are: Designing, implementing, training, and evaluating model routers that select an appropriate model or reasoning profile for each turn Developing portable provider and protocol abstractions that preserve authentication, telemetry, cache and context signals, and execution provenance Defining versioned schemas and contracts for models, provider offers, agents, workspaces, skills, actions, tools, memories, and trajectories Building systems that discover, package, adapt, and validate agent skills across coding agents, editors, and other harnesses Researching user-owned memory, scoped identity, trajectory checkpoints, terminal outcomes, retrieval quality, and reviewable context compaction Creating benchmark suites and evaluation protocols for quality, cost, latency, reliability, safety, and portability Designing held-out, out-of-domain, and change-impact evaluations that test new or removed models, providers, skills, and harness versions Investigating distillation, self-improving harnesses, multi-agent training, agent factories, and automated skill creation Writing robust research software, APIs, integration layers, and test infrastructure that enable rapid but reproducible experimentation Collaborating across research and engineering teams to translate promising ideas into secure, reversible, and reliable systems Communicating results through technical reports,