Senior ML Engineer

Jobgether; Schweiz; Vor Ort; CHF 140.000 - 210.000; Vollzeit

Benefits dieser Stelle

Competitive compensation adjusted to 3

Equity where applicable

Mentorship and knowledge sharing

Zusammenfassung

Jobgether is seeking a Senior ML Engineer based in Switzerland to own end-to-end ML initiatives in a high-impact healthcare setting. You will design agentic LLM systems with tool use, retrieval, and orchestration, and lead evaluation and production deployment alongside Product, Clinical, and Engineering teams.

You’ll mentor engineers, contribute to engineering excellence, and help translate clinical needs into scalable technical solutions while collaborating across Europe.

Qualifikationen

• Proven experience shipping production ML 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.
• 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.

Aufgaben

• 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.

Kenntnisse

Production ML systems

LLM-based systems

Prompting

Retrieval

Tool calling

Agent workflows

Evaluation frameworks

Software engineering

Communication

Team collaboration

Tools

Python

PyTorch

Distributed systems

Jobbeschreibung

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 Switzerland.

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.