Lead Machine Learning Engineer

TBAuctions; Amsterdam; On-site; EUR 100,000 - 120,000; Full time

Benefits offered by this job

Bonus scheme

Pension scheme

25 vacation days

Laptop and iPhone

Training opportunities

Job summary

TBAuctions is building an AI‑driven auction platform in Amsterdam. We seek a Lead Machine Learning Engineer to head the ML & AI team, shaping technical direction while remaining hands‑on.

You will own end‑to‑end architecture, MLOps, and real‑time personalization at scale, guiding a growing team of ML engineers. You will design scalable systems, mentor engineers, and work with product managers to deliver measurable business impact.

Qualifications

• 7+ years in ML engineering, including 2 years leading technical direction of teams.
• Master’s in Computer Science, Statistics, Mathematics, or a related field.
• Experience with recommender systems at scale: retrieval and candidate generation, ranking models, and real‑time personalization in high‑load production environments.
• A track record of building and operating ML/AI systems in production, across the full lifecycle: feature engineering, model development, deployment and monitoring.
• Strong systems, architecture and MLOps background: distributed, event‑driven ML systems, API and model serving as microservices, containerization and orchestration with Kubernetes / AKS, and CI/CD and experiment tracking.
• Strong grounding in experimentation and statistics: metric design, A/B testing and causal inference to quantify business impact.
• Expertise in Python and PySpark, with a solid grasp of ML algorithms and frameworks (MLlib, Scikit‑learn, TensorFlow/PyTorch, MLflow). Familiarity with Java/Kotlin is a plus.
• Experience in building AI agents and LLM‑based products in production, including memory, context engineering, retrieval and orchestration, and multimodal retrieval systems/architecture.
• Comfortable splitting your time between building directly and leading, and happy to stay hands‑on in the code and architecture.
• Excellent communication skills, able to explain complex technical choices and trade‑offs to non‑technical stakeholders.

Responsibilities

• Own the architecture of ML & AI systems end to end: data pipelines, feature and model stores, training and serving infrastructure, built as reusable capabilities our auction brands can share.
• Design the architecture and orchestration of our AI agents platform, with strong evaluation, monitoring and guardrails for reliable behaviour in production.
• Strengthen and contribute to our existing MLOps setup: automated training, validation, deployment and monitoring, experiment tracking and CI/CD.
• Design and build our recommendation, search and ranking systems end to end: candidate generation and retrieval, ranking models, embeddings and vector search, and real‑time personalization at scale.
• Deploy models as robust APIs and scalable microservices, containerized and orchestrated with Kubernetes.
• Design systems, write and review code in a modern stack, and raise the bar on engineering best practices.
• Identify and prioritize the highest‑impact opportunities across our auction brands and landscape, set and own the ML & AI roadmap.
• Frame business problems as ML tasks, define most impactful business metrics, and run rigorous experiments (A/B tests) to measure real impact.
• Drive continuous, incremental improvement of our ML & AI products, balancing near‑term delivery with longer‑term investment in the platform and innovation.
• Work closely with product managers and other tech leads to align priorities and delivery across teams.
• Manage business stakeholders and align them on priorities, scope and trade‑offs, translating complex technical concepts to non‑technical audiences effectively.
• Lead, mentor, hire and grow a team of ML engineers, supporting their career development and upskilling, and ensure chapter growth.

Skills

Machine learning

Leadership

Python

PySpark

TensorFlow

PyTorch

Experimentation

Communication

Java/Kotlin

Education

Master’s degree in CS/Statistics/Math

Tools

Kubernetes

AKS

Databricks

Airflow

DBT

FastAPI

MLflow

TensorFlow

PyTorch

Job description

At TBAuctions, we are building the intelligent auction platform behind our brands, with machine learning and AI at its core: powering recommendations, search and personalization, valuation, supply-demand matching and streamlining processes with AI agents across millions of auctions.

We are looking for a Lead Machine Learning Engineer to head up our ML & AI team and the platform behind it, in a role that is equal parts hands‑on and leadership. The ideal candidate has led and delivered large‑scale ML/AI projects to production with real business impact, shaped technical direction, and built strong teams.

What you will do

Build and architect

• Own the architecture of our ML & AI systems end to end: data pipelines, feature and model stores, training and serving infrastructure, built as reusable capabilities our auction brands can share.

• Design the architecture and orchestration of our AI agents platform, with strong evaluation, monitoring and guardrails for reliable behaviour in production.

• Strengthen and contribute to our existing MLOps setup: automated training, validation, deployment and monitoring, experiment tracking and CI/CD.

• Design and build our recommendation, search and ranking systems end to end: candidate generation and retrieval, ranking models, embeddings and vector search, and real‑time personalization at scale.

• Deploy models as robust APIs and scalable microservices, containerized and orchestrated with Kubernetes.

• Design systems, write and review code in a modern stack, and raise the bar on engineering best practices.

Lead and set direction

• Identify and prioritize the highest‑impact opportunities across our auction brands and landscape, set and own the ML & AI roadmap.

• Frame business problems as ML tasks, define most impactful business metrics, and run rigorous experiments (A/B tests) to measure real impact.

• Drive continuous, incremental improvement of our ML & AI products, balancing near‑term delivery with longer‑term investment in the platform and innovation.

• Work closely with product managers and other tech leads to align priorities and delivery across teams.

• Manage business stakeholders and align them on priorities, scope and trade‑offs, translating complex technical concepts to non‑technical audiences effectively.

• Lead, mentor, hire and grow a team of ML engineers, supporting their career development and upskilling, and ensure chapter growth.

What you bring

• 7+ years in ML engineering, including 2 years leading technical direction of teams (formal people management is a plus).

• Master’s in Computer Science, Statistics, Mathematics, or a related field.

• Experience with recommender systems at scale: retrieval and candidate generation, ranking models, and real‑time personalization in high‑load production environments.

• A track record of building and operating ML/AI systems in production, across the full lifecycle: feature engineering, model development, deployment and monitoring.

• Strong systems, architecture and MLOps background: distributed, event‑driven ML systems, API and model serving as microservices, containerization and orchestration with Kubernetes / AKS, and CI/CD and experiment tracking.

• Strong grounding in experimentation and statistics: metric design, A/B testing and causal inference to quantify business impact.

• Expertise in Python and PySpark, with a solid grasp of ML algorithms and frameworks (MLlib, Scikit‑learn, TensorFlow/PyTorch, MLflow). Familiarity with a language such as Java or Kotlin is a plus.

• Experience in building AI agents and LLM‑based products in production, including memory, context engineering, retrieval and orchestration, and multimodal retrieval systems/architecture.

• Comfortable splitting your time between building directly and leading, and happy to stay hands‑on in the code and architecture.

• Excellent communication skills, able to explain complex technical choices and trade‑offs to non‑technical stakeholders.

What we offer you

• A hands‑on leadership role with real ownership, within an entrepreneurial and innovative organization

• A modern data stack (FastAPI, Databricks, Airflow, DBT) and cloud‑native deployment on Azure with Kubernetes (AKS).Independent and varied role within an entrepreneurial and innovative organization

• Gross yearly salary of € 100.000 - € 120.000 (including holiday allowance), depending on experience

• Bonus scheme

• Pension scheme

• 25 vacation days

• Laptop and iPhone

• Training opportunities

• Working in a team of professionals, where hard work is well combined with humor