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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Impellam Group plc - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Python (Programming Language), Machine Learning, Operational Data Store, Software Engineering, Model Validation, Code Testing, Operational Systems - **Published:** August 4, 2026 - **Apply:** https://computerjobs.com/us/en/mob/job/36F1779E263C3F07CC ## About the Role This role suits a senior ML practitioner who values judgement, evidence, and outcomes over theoretical or tooling purity., * Proven experience operating at senior practitioner level as a Machine Learning Engineer, AI Engineer, Applied ML Scientist, or equivalent * Strong grounding in applied mathematics, statistics, and scientific practice * Demonstrated ability to evaluate ML models using quantitative evidence and structured experimentation * Excellent Python skills for building, evaluating, and iterating on ML solutions * Experience working with real-world, imperfect data from operational systems * Strong software engineering practices, including readable, maintainable, and well-tested code * Experience integrating ML components into broader production systems * Clear understanding of data ethics, privacy, and responsible use of data * Strong communication skills across technical and non-technical audiences * Proven ability to lead work independently and take ownership of outcomes Technologies you'll encounter The environment evolves, but typical tools include: * Python for experimentation, modelling, and evaluation * Weights & Biases (or equivalent) for experiment tracking * AWS, including services such as SageMaker and Bedrock * Internally supported AI development platforms and tooling This role is not suited to candidates who are dogmatic about specific tools. Adaptability and outcome focus matter more than platform allegiance. Desirable experience * Working in secure, safety-critical, or heavily regulated environments * Background in sectors such as energy, oil & gas, defence, or public sector * Experience within formal technical assurance or governance processes * Collaboration with external suppliers, partners, or research organisations * Comfort operating at pace where quality, safety, and compliance are non-negotiable If you enjoy solving complex problems, applying scientific thinking to messy reality, and delivering ML that stands up to scrutiny, this role offers both challenge and meaning. If interested, apply now! ## Description * Apply mathematical, statistical, and scientific reasoning to form hypotheses, quantify uncertainty, and interpret results * Design and run structured experiments to assess model behaviour, performance, and user impact * Work with real, imperfect operational data, not just curated or static datasets * Collect, assess, and transform data to support model evaluation and continuous improvement * Balance rigour with pragmatism, delivering solutions that are robust, proportionate, and fit for purpose * Integrate machine learning components into wider systems, considering performance, reliability, and operational constraints * Communicate complex technical ideas clearly to non-technical stakeholders, enabling informed decision-making * Engage confidently in deep technical design and review discussions with peers * Operate effectively within a strong technical assurance and review culture * Collaborate with internal teams and selected external partners working at the leading edge of AI ## Related Videos - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Introduction to Responsible AI: Balancing Value and Risk](https://www.wearedevelopers.com/videos/1972-introduction-to-responsible-ai-balancing-value-and-risk) - [Dirty Tests And How To Clean Them](https://www.wearedevelopers.com/videos/515-dirty-tests-and-how-to-clean-them) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Optimizing your AI/ML workloads for sustainability](https://www.wearedevelopers.com/videos/570-optimizing-your-ai-ml-workloads-for-sustainability) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Prompt Engineering is a Job of the Past](https://www.wearedevelopers.com/magazine/342-prompt-engineering-is-a-job-of-the-past) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)