Senior AI Research Engineer
New
J
JobgetherAI Research / Industrial Automation
Based in SpainFull-TimeSenior
Salary£92,065 to £173,648
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Job Details
- Experience
- 4+ years
- Required Skills
- DockerPythonKubernetesMachine LearningMLFlowPyTorchC++RustMLOpsDistributed Systems
Requirements
- 4+ years of relevant professional experience in software engineering, machine learning engineering, MLOps, or related technical fields.
- Proven experience leading technical projects and owning delivery from initial concept through implementation.
- Previous experience working in machine learning research and development environments, ideally connecting research initiatives with production systems.
- Strong understanding of machine learning and MLOps concepts, including experiment tracking, model lifecycle management, deployment processes, and systems involving non-deterministic components.
- Strong programming skills in Python and familiarity with lower-level programming languages such as C++ or Rust.
- Solid engineering foundation combined with scientific understanding in areas such as machine learning, optimization, control systems, or physical sciences.
- Experience designing scalable infrastructure for AI workloads, distributed computing, or cloud-based environments.
- Strong problem-solving abilities, curiosity, and willingness to explore unfamiliar technical domains.
- Excellent organizational, communication, and collaboration skills in a remote and international environment.
- Alignment with values centered around transparency, collaboration, ownership, operational excellence, and empathy.
Responsibilities
- Own and evolve research infrastructure end-to-end, including experiment orchestration, distributed training, model tracking, evaluation workflows, and automated deployment systems.
- Build and scale distributed computing solutions for machine learning workloads, including multi-node GPU environments, data pipelines, and cost-efficient infrastructure management across cloud platforms.
- Improve research and development velocity through performance engineering, including optimizing simulators, training pipelines, profiling bottlenecks, and implementing scalable solutions.
- Act as a bridge between research and production engineering teams, helping transform AI breakthroughs into reliable production-ready systems.
- Develop a deep understanding of internal platforms, tools, and technical capabilities to support effective customer-facing solutions.
- Maintain clear documentation of research projects, engineering decisions, products, and operational processes.
- Contribute to medium- and long-term technical decisions that shape research infrastructure and engineering strategy.
- Lead projects from concept to delivery, taking ownership of execution, prioritization, and successful outcomes.
- Mentor team members, share technical knowledge, and support collaborative problem-solving across engineering teams.
- Continuously improve development practices, tooling, and infrastructure to accelerate AI research and deployment.
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