Senior ML Engineer – AI Research, Portability

This is a fully remote position, open to applicants in Netherlands, +1 more country.

📋 Description

• Create and develop research prototypes and robust systems that integrate models, providers, and agent runtimes.

• Formulate research inquiries and establish evaluation methodologies.

• Experiment with concepts in realistic agent workflows and transform promising outcomes into reusable components.

• Conduct research on model routing, provider and protocol portability, agent interoperability, portable memory and context, agent interchange standards, and optimization techniques for agents.

• Design, implement, train, and assess model routers.

• Develop portable abstractions for providers and protocols that maintain authentication, telemetry, cache and context signals, and execution provenance.

• Define versioned schemas and contracts for models, providers, agents, workspaces, skills, actions, tools, memories, and trajectories.

• Construct systems for discovering, packaging, adapting, and validating agent skills across coding agents, editors, and other harnesses.

• Investigate user-owned memory, scoped identity, trajectory checkpoints, retrieval quality, and context compaction.

• Establish benchmark suites and evaluation protocols focusing on quality, cost, latency, reliability, safety, and portability.

• Perform held-out, out-of-domain, and change-impact evaluations.

• Explore distillation, self-improving harnesses, multi-agent training, agent factories, and automated skill generation.

• Develop robust research software, APIs, integration layers, and reproducible testing infrastructure.

• Collaborate across various teams including research, infrastructure, security, product, and engineering.

• Present findings through technical reports, demonstrations, open-source releases, benchmarks, and research papers.

⛳️ Requirements

• In-depth knowledge of machine learning, large language models, or statistical decision-making.

• Extensive expertise in at least one pertinent area such as model routing, recommender systems, agent systems, retrieval and memory, model evaluation, distributed systems, or protocol and API design.

• Experience in constructing and assessing modern language-model or agentic systems, including tool utilization and multi-turn workflows.

• Proficient in designing, executing, and analyzing machine learning experiments with appropriate statistical rigor.

• Ability to develop meaningful research questions, design hypothesis-driven experiments, and derive defensible conclusions.

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

• Strong software engineering and algorithm design capabilities.

• Excellent Python skills and experience working with production systems.

• Familiarity with APIs, data schemas, distributed services, testing, observability, code reviews, and CI/CD practices.

• Ability to analyze security, privacy, provenance, permissions, failure modes, and user control in agent systems.

• Experience in implementing research ideas across modeling, data, systems, and evaluation.

• Strong communication skills and technical leadership qualities.

• Preferable experience with model routers, cascades, mixture-of-experts systems, recommenders, or cost-aware inference.

• Preferable experience in integrating multiple model providers or inference stacks.

• Preferable familiarity with agent harnesses, coding agents, editor integrations, function calling, tool execution, MCP, or agent-to-agent protocols.

• Preferable experience with retrieval systems, vector search, knowledge graphs, temporal data, memory architectures, or context management.

• Preferable experience with benchmark suites, distillation, reinforcement learning, preference learning, reward modeling, automated skill generation, secure authentication, sandboxing, privacy-preserving telemetry, provenance, policy-enforced execution, and distributed systems.

• Preferable proficiency in TypeScript, Go, Rust, or another systems language in addition to Python.

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

• Exceptional command of English, with strong technical writing, presentation, and communication skills.

• Proficient in version control, testing, code review, and CI/CD processes.

🏝️ Benefits

• Competitive compensation.

• Opportunities for career growth and learning.

• Flexibility and ownership in your role.

• Collaborative and innovative work culture.

• Chance to work on impactful AI projects.

• An international environment surrounded by talented teams.

• A fast-paced working atmosphere.

• Encouragement of bold thinking.

• Constant opportunities for growth.

• A chance to make a meaningful impact.

• Trust and genuine ownership in your work.

• Opportunity to influence the future of AI.

• Commitment to equal employment opportunities.

• Accommodations provided during the application process.