Senior ML Engineer
Sword Health · Location not mentioned
What the posting says
Each fact carries its state:Statedfound in the postingConflictstwo statements disagree, both quotedNot mentionedthe posting does not say
- PayStated
- EUR 66,500–EUR 104,500 a year
- Employment typeStated
- Full time
- Work arrangementStated
- Remote OK
Not mentioned in this posting
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- ShiftNot mentioned
- The posting does not say
- ExperienceNot mentioned
- The posting does not say
- EducationNot mentioned
- The posting does not say
- Visa sponsorshipNot mentioned
- The posting does not say
Requirements, each with its sentence from the posting
These are the employer’s words. The index does not judge whether you meet them.
- ML systems in productionrequired
Experience shipping ML systems to production that people actually depend on
from the posting - LLM work in productionrequired
Hands-on LLM work in production: prompting, retrieval, tool calling, and agent-style workflows
from the posting - Evaluation frameworks and datasetsrequired
you've built eval datasets and frameworks, and you can tell a real improvement from noise
from the posting - ML fundamentalsrequired
Strong ML fundamentals: you know which approach fits which problem
from the posting - Production-quality coderequired
Solid engineering skills: production-quality code, familiarity with distributed systems
from the posting - Distributed systemsrequired
familiarity with distributed systems, and the patience to debug messy ML pipelines
from the posting - Communication with technical and clinical stakeholdersrequired
Clear communication with both technical and clinical stakeholders
from the posting - Fine-tuning or preference optimizationpreferred
Experience with fine-tuning or preference optimization (RLHF, DPO, or similar)
from the posting - Healthcare AIpreferred
Healthcare AI, or other high-stakes domains where errors carry real cost
from the posting - Agent frameworks or evaluation toolingpreferred
Built agent frameworks or evaluation tooling from scratch
from the posting - Open source contributions or knowledge sharingpreferred
Open source contributions, technical writing, or other knowledge sharing
from the posting
Read the full posting as the employer wrote it
At Sword, we’re building AI to heal billions and unlock humanity’s full potential. In doing so, we’re pioneering AI Care, a fundamentally new approach to healthcare built for medical reasoning, safety, and real-time treatment, not generic technology applied after the fact. As both a clinical-centric frontier AI lab and an applied AI platform, Sword is reimagining how care is delivered at scale, removing traditional barriers like appointments, waiting rooms, and stigma so more people can access the care they need—and ultimately get back to lives lived in full.
Since 2020, Sword has expanded across physical therapy, women’s health, cardiometabolic, and mental health, and is now moving beyond the session to a fully AI-native, 24/7 care program that brings physical activity, therapeutic exercise, psychotherapy, nutrition, and behavior change into one connected experience. More than 700,000 members across three continents have completed over 10 million AI sessions, helping 1,000+ enterprise clients avoid more than $1 billion in unnecessary healthcare costs. Backed by 42 clinical studies, 44+ patents, and more than $500 million raised from leading investors including Khosla Ventures, General Catalyst, and Founders Fund, Sword is defining a new standard for healthcare.
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.
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Explorer (Level 1) — Uses AI daily to boost personal productivity
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Builder (Level 2) — Creates workflows and tools that elevate the whole team
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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: you've 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: you've 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.
*The range below reflects base, variable, and equity for this role.
This is a starting point, not a ceiling — once someone joins and proves they're outlier talent, we adjust quickly to make sure their compensation matches their impact.
Job titles may span more than one career level, so actual pay depends on skills, qualifications, experience, location, and market demand, among other factors. The range reflects base salary plus any variable, bonus, or sales incentives, and our estimate of the value of private company stock options where applicable. It's subject to change, and future stock value isn't guaranteed. Sword also offers additional benefits beyond total compensation.
The posting text is reproduced as the employer wrote it; the index adds nothing to it.