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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 ML Engineer based in Ireland. This role offers the opportunity to build production-grade AI systems in a high-impact healthcare environment where reliability, evaluation, and safety are essential. You’ll own machine learning initiatives end-to-end, taking complex problems from early exploration through deployment and continuous improvement. A major focus will be designing agentic LLM systems with tool use, retrieval, orchestration, and robust evaluation frameworks. You’ll work across the AI stack, from data preparation and model adaptation to serving, monitoring, and production feedback loops. Close collaboration with Product, Clinical, and Engineering teams will be critical to translating real-world healthcare needs into effective technical solutions. You’ll also contribute to engineering excellence through mentorship, knowledge sharing, and practical adoption of AI across the team. Accountabilities Own machine learning projects end-to-end, from problem exploration and experimentation through production deployment, monitoring, and ongoing iteration. Design and build reliable agentic LLM systems using multi-step workflows, tool calling, retrieval, and orchestration techniques suitable for high-stakes clinical environments. Develop robust evaluation strategies, including evaluation datasets, offline and online testing harnesses, LLM-as-judge pipelines with human review, and regression testing. Improve model performance using evidence-driven approaches such as prompt engineering, retrieval optimization, distillation, fine-tuning, or other appropriate techniques. Work across the full machine learning lifecycle, including data preparation, model adaptation, serving, monitoring, and production feedback loops. Partner with Product, Clinical, and Engineering stakeholders to translate clinical requirements into technical solutions and identify trade-offs early. Review code, share technical knowledge, contribute to engineering best practices, and mentor less experienced engineers. Continuously explore and apply AI-assisted approaches that improve individual productivity, team workflows, products, and engineering processes. Requirements Proven experience shipping production machine learning systems that are relied upon by real users or customers. Hands-on experience developing and deploying LLM-based systems, including prompting, retrieval, tool calling, and agent-style workflows. Strong evaluation expertise, with experience building evaluation datasets, frameworks, testing methodologies, and mechanisms for distinguishing meaningful improvements from statistical or operational noise. Strong foundations in machine learning and the ability to select appropriate approaches based on technical requirements, evidence, and trade-offs. Experience working with ambiguous or loosely defined problems and transforming them into reliable production solutions. Strong software engineering skills, including writing production-quality code, working with distributed systems, and debugging complex machine learning pipelines. Clear communication skills and the ability to collaborate effectively with both technical and clinical stakeholders. Demonstrated AI fluency, with at least Level 1 proficiency: using AI regularly to improve personal productivity. More senior expectations may include building AI-enabled workflows or embedding AI into products and processes. Experience with fine-tuning or preference optimization techniques such as RLHF or DPO is a plus. Experience in healthcare AI or other high-stakes domains where system errors can have significant consequences is advantageous. Experience building agent frameworks or evaluation tooling from scratch is a plus. Open-source contributions, technical writing, or other forms of technical knowledge sharing are valued. Willingness to work within a distributed European environment, with the position open to candidates across Europe. Benefits Competitive compensation adjusted according to the local market and cost of living in the European country where you are hired. Total compensation may include base salary, variable compensation, bonuses or incentives, and equity where applicable. Compensation is reviewed based on skills, qualifications, experience, location, market conditions, and demonstrated impact. Opportunity for compensation growth as your responsibilities and contribution increase. Country-specific benefits and perks aligned with local regulations and market practices. Opportunity to work on high-impact AI systems addressing complex healthcare challenges. Exposure to cutting-edge LLMs, agentic AI, evaluation frameworks, and production machine learning at significant scale. Collaborative environment spanning AI, engineering, product, and clinical expertise. Opportunity to mentor other engineers and contribute to technical practices and knowledge sharing. European-wide hiring flexibility, with employment arrangements and benefits adapted to the country of hire. 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.