Hiring

Full-time: Senior/Staff Machine Learning Engineer (AI Engineer, Applied Scientist)

System design and architectural ownership for ML systems.

Focus: Search/Ranking, RecSys, GenAI/RAG, ML Platform, MLOps.

Best fit areas

Retrieval + ranking, embeddings, LTR. Latency/cost/reliability. Degradation, rollbacks. Quality metrics and experiment loops.

RFC/ADR ownership

Candidate generation + ranking, personalization. A/B testing, incrementality, product metrics. Feature pipelines, real-time signals.

Offline eval + online experiments

Eval, retrieval + rerank + grounding. Hallucinations, safety, policy. Quality gates and quality monitoring.

Degradation playbooks

Data/feature pipelines, training, serving. Observability, CI/CD for models, reliable releases. Scale, cost control, reproducibility.

Observability as design

On-prem voice AI operator handles 72% of calls without human in 0.96s with 58% cost reduction.

Client: NDA Domain: FinTech

Internal tool for profiling latency, throughput, and $/req of models in production

Client: NDA Domain: E-commerce

MVP chat search with deployment automation, experiments, and quality monitoring

Client: NDA Domain: E-commerce

Fraud detection system reduces inefficient spending by 24% and automates verification of 100 channels in 12 minutes

Client: NDA Domain: AdTech

If reaching out: role, location, team, 2-3 key requirements.

My current time: --:-- Amsterdam

Senior/Staff ML Engineer. System design, ownership, high-traffic ML systems.

Fastest way