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Jobgether

Senior AI Research Engineer

United Kingdom
£92k – £173.7k/yr
Posted about 16 hours ago
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This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Research Engineer based in the United Kingdom.

This role offers the opportunity to build and scale the infrastructure powering next-generation AI solutions for industrial automation.

You will work at the intersection of machine learning engineering, MLOps, and research, enabling teams to move advanced AI concepts into production faster.

The position focuses on designing reliable systems, optimizing research workflows, and supporting the deployment of intelligent control technologies.

You will collaborate with researchers, engineers, and cross-functional teams to transform complex AI experiments into impactful real-world applications.

With ownership across the research-to-production lifecycle, you will influence technical direction while solving challenging engineering problems at scale.

This is an ideal opportunity for an experienced engineer passionate about AI, distributed systems, and creating technology with measurable real-world impact.

Accountabilities

  • 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.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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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.

Preferred Skills & Experience

  • Experience with machine learning research, AI systems, or MLOps-focused engineering.
  • Familiarity with reinforcement learning, simulation environments, or control systems.
  • Experience using distributed computing frameworks such as Ray and managing GPU workloads across multiple nodes.
  • Knowledge of platforms and tools such as PyTorch, SciPy, scikit-learn, NumPy, pandas, MLflow, Docker, Kubernetes, and cloud infrastructure.
  • Understanding of industrial systems, including heating, cooling, manufacturing, or data center environments.
  • Scientific or technical background that enables effective collaboration with research-focused teams.

Benefits

  • Competitive base salary ranging from £92,065 to £173,648, depending on location tier, experience, qualifications, and other relevant factors.
  • Eligibility for meaningful equity participation.
  • Fully remote work environment with flexibility across different locations and time zones.
  • Medical, dental, and vision insurance, with benefits varying by region.
  • Unlimited paid time off with a required minimum of 20 days per year.
  • Paid parental leave, depending on regional policies.
  • Flexible stipends supporting workspace setup, personal well-being, and continued professional development.
  • Company-provided MacBook.
  • Training programs covering technical development, customer immersion, and professional growth.
  • Opportunity to work in a fast-paced, collaborative environment where your contributions directly influence technical direction.
  • Strong remote culture based on documentation, asynchronous collaboration, regular communication, and virtual team-building activities.
  • Significant ownership opportunities and the chance to contribute to impactful AI-driven solutions.

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How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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Skills

Machine Learning Engineering
MLOps
Distributed Computing
Python
C++
Rust
PyTorch
Kubernetes
Docker
Ray
Model Tracking
Experiment Orchestration
Cloud Infrastructure
Performance Engineering
Control Systems
Reinforcement Learning

Location

United Kingdom

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