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