Senior ML Engineer (Europe-based/Remote)

Team: AI : Applied AI

Location: Europe (Remote)

Commitment: Full Time

Workplace Type: remote

AI Proficiency at Sword

AI fluency is a core expectation at Sword. Every candidate is assessed against our three-level framework — be ready to share real examples of how AI is already part of how you work.

• Explorer (Level 1) — Uses AI daily to boost personal productivity

• Builder (Level 2) — Creates workflows and tools that elevate the whole team

• Integrator (Level 3) — Embeds AI into products and processes at scale

Every hire must demonstrate at least Level 1. The expected level will vary depending on the seniority of the role.

What you’ll be doing:

Own ML projects end-to-end: take problems from exploration through production deployment and keep iterating once real users are on them;
Build agentic LLM systems: design multi-step workflows with tool use, retrieval, and orchestration, and make them reliable enough for clinical settings;
Treat evaluation as core engineering work: build eval sets, offline and online harnesses, LLM-as-judge pipelines with human review, and regression tests that catch quality drops before they reach users;
Improve model quality with whatever fits the problem: prompting, retrieval, distillation, or fine-tuning, chosen on evidence rather than habit;
Work across the full AI stack: data prep, model adaptation, serving, monitoring, and the feedback loops that keep systems improving in production;
Partner with Product, Clinical, and Engineering: translate clinical requirements into technical decisions and surface tradeoffs early;
Help the team get better: review code, share what you learn, and mentor engineers earlier in their careers.

What you need to have:

• Experience shipping ML systems to production that people actually depend on;
• Hands-on LLM work in production: prompting, retrieval, tool calling, and agent-style workflows;
• A rigorous approach to evaluation: youve built eval datasets and frameworks, and you can tell a real improvement from noise;
• Strong ML fundamentals: you know which approach fits which problem and can reason clearly about tradeoffs;
• Comfort with ambiguity: youve taken loosely defined problems and turned them into something running in production;
• Solid engineering skills: production-quality code, familiarity with distributed systems, and the patience to debug messy ML pipelines;
• Clear communication with both technical and clinical stakeholders.

Bonus points

• Experience with fine-tuning or preference optimization (RLHF, DPO, or similar);
• Healthcare AI, or other high-stakes domains where errors carry real cost;
• Built agent frameworks or evaluation tooling from scratch;
• Open source contributions, technical writing, or other knowledge sharing.