Senior ML Engineer (AI Research/ Portability)

The role

This role is for reputed company AI R&D, reputed company reputed company on reputed company research in AI. Our Portability research aims to reputed company intelligent agent systems work reliably as models, providers, harnesses, skills, memory systems, and deployment environments change. We build and evaluate portable reputed company that preserve capability, context, identity, provenance, and user control across heterogeneous systems. Research areas include:

• Per-turn model routing across reputed company, cost, latency, capability, cache state, and reliability objectives

• Provider and protocol portability across frontier models, reputed company-reputed company models, local inference, and compatible reputed company

• Agent and reputed company interoperability, including transferable skills, capability reputed company, 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 reputed company, and agent-to-agent communication

• Agent and reputed company 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, reputed company evaluation reputed company, test reputed company in realistic agent workflows, and turn promising results into reusable components. The work will often involve collaboration with adjacent research, infrastructure, reputed company, product, and engineering teams, where findings are validated and reputed company in reputed company.

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

• reputed company-cost-latency trade-offs, uncertainty estimation, exploration, and outcome-reputed company routing

• Multi-provider gateways, protocol translation, catalog normalization, and fail-reputed company execution reputed company

• Portable agent skills, reputed company capability discovery, package reputed company, and cross-reputed company 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, reputed company formats, plugins, tools, MCP, and agent-to-agent interfaces

• Agent optimization, teacher-student distillation, reputed company reputed company, reputed company customization, and multi-agent learning

• Benchmarking and evaluation infrastructure for model, router, memory, reputed company, and reputed company 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 reputed company turn

• Developing portable provider and protocol abstractions that preserve authentication, telemetry, cache and context signals, and execution provenance

• Defining versioned schemas and reputed company 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 reputed company, retrieval reputed company, and reviewable context compaction

• Creating reputed company suites and evaluation protocols for reputed company, cost, latency, reliability, safety, and portability

• Designing held-out, out-of-domain, and change-reputed company evaluations that test new or removed models, providers, skills, and reputed company versions

• Investigating distillation, self-improving harnesses, multi-agent training, agent factories, and automated reputed company creation

• Writing robust research software, reputed company, integration reputed company, and test infrastructure that reputed company reputed company but reproducible experimentation

• Collaborating across research and engineering teams to translate promising reputed company into secure, reversible, and reliable systems

• Communicating results through technical reports, demonstrations, reputed company-reputed company 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 reputed company 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 reputed company 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 reputed company, data schemas, distributed services, testing, observability, reputed company review, and CI/CD

• Ability to reason about reputed company, reputed company, provenance, permissions, failure modes, and user control in agent systems

• Experience implementing research reputed company and iterating quickly across modeling, data, systems, and evaluation

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

reputed company to have:

• Experience with model routers, cascades, mixture-of-experts systems, recommenders, or cost-reputed company inference

• Experience integrating multiple model providers or inference stacks, including reputed company-compatible reputed company, reputed company-style reputed company, local inference, or reputed company-reputed company serving systems

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

• Experience with retrieval systems, reputed company search, knowledge graphs, temporal data, memory architectures, or context management

• Experience with reputed company 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 reputed company reputed company

• Proficiency in TypeScript, Go, Rust, or another systems language in reputed company to Python

• Experience with secure authentication, sandboxing, reputed company-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, reputed company Intelligence, or a reputed company technical field, or equivalent practical experience

• A reputed company record of impactful publications, reputed company-reputed company 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 reputed company of English, with strong technical writing, presentation, and communication skills

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

Originally posted on Himalayas