Founding Senior ML Engineer at Afori

✨ The Stack take on this Afori job

• 🏢 Afori has 2 jobs here, 2 with visa sponsorship see all
• 🛂 Visa: Highly Qualified Professional permit. Education: Higher education degree guide
• 💡 No salary shown. Ask Afori if it is above the minimum.

Updated 2026-11-03, from our data and official sources.

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About the role

Responsibilities:
- Design and own the continuous learning (learning flywheel) architecture for agent swarms: capture agent outcomes, expert signals and case feedback to drive iterative model improvements.
- Build and operate fine-tuning and distillation pipelines for German insurance NLP tasks: dataset curation, training, validation, and staged rollouts.
- Define and implement evaluation standards: gold sets, benchmarks, abstention/faithfulness checks, and production eval gates.
- Develop model serving and cost-aware inference strategies to run at scale across thousands of real Cases in a regulated environment.
- Create cross-sell intelligence models that score opportunities from APDB/account data and feed actionable signals into broker workflows.
- Collaborate with product, infra, legal and ops to ensure compliance, observability and safe deployments; monitor production performance and iterate.

Requirements:
- Proven experience building and shipping ML systems in production, including LLM fine-tuning and model distillation workflows.
- Hands-on expertise designing evaluation methodologies (gold sets, benchmarks, abstention checks) and enforcing eval gates for production.
- Strong ML engineering fundamentals: training pipelines, serving infrastructure, monitoring and cost-aware inference at scale.
- Track record owning ambiguous end-to-end problems in early-stage or fast-moving product contexts; bias for finishing and operational rigor.
- Experience with NLP in regulated domains, agentic systems, continuous learning loops, or German-language models is a strong plus.
- Fluent Python and familiarity with model/tooling ecosystem (open-weight and proprietary models); good trade-off judgment between performance, cost and safety.

Responsibilities

Design and own the continuous learning loop for agent swarms and manage the fine-tuning and distillation pipeline for specialized NLP tasks. Establish evaluation methodologies and serving infrastructure to ensure ML models meet production standards.

Requirements

Requires a strong track record in building and shipping ML systems, specifically with LLM fine-tuning and evaluation. Candidates must possess solid ML engineering fundamentals and the ability to own ambiguous problems in an early-stage environment.

Key skills

• Machine Learning — in 285 other listings on The Stack
• LLM — in 51 other listings on The Stack
• Fine-tuning — in 7 other listings on The Stack
• Distillation — in 6 other listings on The Stack
• NLP — in 25 other listings on The Stack
• Python — in 1173 other listings on The Stack
• Infrastructure — in 22 other listings on The Stack
• Evaluation methodologies — not in any other listing on The Stack
• Agentic systems
• Continuous learning
• Data modeling
• System architecture

On The Stack (as of 2026-11-03)

• Afori has 1 other listing on The Stack, 1 of them marked as offering visa sponsorship.
• 67 visa-sponsored jobs in Spain on The Stack, posted in the last 60 days.
• 29 visa-sponsored machine learning engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about Afori

Is this Founding Senior ML Engineer role still open?

It was posted on 2026-09-01. Listings more than 30 days old may no longer be active, so check Afori's site before applying.

Does Afori offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Afori's site.

Where is this role located?

The listing gives the location as Spain. It is marked on-site.

What skills does this role ask for?

The listing names: Machine Learning, LLM, Fine-tuning, Distillation, NLP, Python, Infrastructure, Evaluation methodologies, Agentic systems, Continuous learning, Data modeling, System architecture.