Senior ML Engineer (AI Research/ Portability)

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, demonstrations, open-source releases, benchmarks, and research publications

We expect you to have:

A profound understanding of machine learning, large language models, or statistical decision-making

Deep expertise in at least one relevant area, such as model routing, recommender systems, agent systems, retrieval and memory, model evaluation, distributed systems, or protocol and API design

Experience building and evaluating modern language-model or agentic systems, including tool use and multi-turn workflows

Experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor

Ability to formulate meaningful research questions, design experiments that test clear hypotheses, and draw defensible conclusions

Understanding of evaluation leakage, held-out testing, out-of-domain generalization, uncertainty, and reproducibility

Strong software-engineering and algorithm-design skills; excellent Python skills and the ability to work across production systems

Experience with APIs, data schemas, distributed services, testing, observability, code review, and CI/CD

Ability to reason about security, privacy, provenance, permissions, failure modes, and user control in agent systems

Experience implementing research ideas and iterating quickly across modeling, data, systems, and evaluation

Strong communication and technical leadership abilities, including collaboration across research and engineering disciplines and clear documentation of findings in technical reports or research publications

Nice to have:

Experience with model routers, cascades, mixture-of-experts systems, recommenders, or cost-aware inference

Experience integrating multiple model providers or inference stacks, including OpenAI-compatible APIs, Anthropic-style APIs, local inference, or open-source serving systems

Familiarity with agent harnesses, coding agents, editor integrations, function calling, tool execution, MCP, or agent-to-agent protocols

Experience with retrieval systems, vector search, knowledge graphs, temporal data, memory architectures, or context management

Experience with benchmark suites for coding, reasoning, factuality, instruction following, tool use, or multi-turn agent workflows

Experience with teacher-student distillation, reinforcement learning, preference learning, reward modeling, or automated skill generation

Proficiency in TypeScript, Go, Rust, or another systems language in addition to Python

Experience with secure authentication, sandboxing, privacy-preserving telemetry, provenance, or policy-enforced execution

Experience building distributed data-processing, evaluation, model-training, or inference systems

A PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience

A track record of impactful publications, open-source contributions, or deployed AI systems

A record of building and delivering products or research prototypes in a dynamic, startup-like environment

Passion for making advanced AI systems composable, inspectable, user-controlled, and resilient to changing models and platforms

Excellent command of English, with strong technical writing, presentation, and communication skills

Proficiency in contemporary software-engineering practices, including version control, testing, code review, and CI/CD