Senior Machine Learning Engineer

Skillsearch; Helsinki; On-site; EUR 144,000 - 156,000; Full time

Job summary

Skillsearch in Helsinki is seeking an experienced Machine Learning Engineer to build scalable ML infrastructure and serving systems for game-wide personalization at scale. You will partner with data scientists, ML engineers and platform teams to operationalize ML workflows and deploy reliable services.

Focus areas include training, deployment, monitoring, and observability. The role is on-site in Helsinki with a 3-month contract and potential extension, offering exposure to high-traffic,

Qualifications

• 5+ years in Machine Learning Engineering and platform work.
• Experience building scalable ML pipelines and infra.
• Strong cloud and distributed systems background.
• Proven ability to ship reliable, production ML services.

Responsibilities

• Design and build automated, production-grade ML workflows.
• Develop reusable platform capabilities for multiple teams.
• Build shared APIs, services and infrastructure for ML apps.
• Improve standardisation and scalability across ML systems.

Skills

Machine Learning Engineering

ML Platform Engineering

MLOps

Data Infrastructure

Distributed Systems

Tools

Python

Spark

Databricks

Ray

Redis

Tecton

Ray Serve

FastAPI

Triton Inference Server

AWS

GCP

Docker

Kubernetes

Terraform

GitHub Actions

Jenkins

Grafana

Prometheus

MLflow

Weights & Biases

PyTorch

TensorFlow

ONNX

Job description

Are you an experienced Machine Learning Engineer with a passion for building scalable ML platforms, production infrastructure and high-performance serving systems?

We're looking for a Machine Learning Engineer to join a world-class game development company and help shape the next generation of machine learning infrastructure powering large-scale personalization and player experiences across multiple games. This is a unique opportunity to work on systems operating at massive scale, supporting billions of automated decisions and interactions every day.

About The Role

As a Machine Learning Engineer, you will focus on building the infrastructure, tooling and workflows that enable machine learning solutions to move seamlessly from experimentation into production.

Working closely with Data Scientists, ML Engineers, LiveOps teams and platform engineers, you will help create reusable platform capabilities that support training, deployment, serving, monitoring and operations at scale.

This is primarily an ML Platform and Infrastructure role rather than a model research position. We are looking for someone who understands the machine learning lifecycle and enjoys building the systems that make ML reliable, scalable and easy to operate.

What You'll Do

ML Platform & Infrastructure

• Design and build automated, production-grade ML workflows.
• Develop reusable platform capabilities that support multiple teams and use cases.
• Build shared APIs, services and infrastructure for machine learning applications.
• Improve standardisation and scalability across ML systems.
• Help define platform architecture and engineering standards.

Training & Deployment Pipelines

• Build and optimise workflows covering:
  • Feature processing
  • Data pipelines
  • Model training
  • Deployment
  • Inference
  • Monitoring
• Improve automation across the machine learning lifecycle.
• Support reproducibility and operational reliability for ML workloads.

Real-Time & Large-Scale Systems

• Develop and operate:
  • Batch processing systems
  • Near real-time pipelines
  • Real-time ML services
• Design low-latency serving systems.
• Optimise performance and scalability for high-volume traffic environments.
• Support infrastructure serving hundreds of millions of users globally.

Reliability & Operations

• Improve observability, monitoring and operational tooling.
• Enhance CI/CD pipelines and deployment workflows.
• Build systems that are robust, maintainable and operationally efficient.
• Support incident management and production troubleshooting.
• Improve testing, reliability and deployment confidence across ML services.

Collaboration

• Partner with Data Scientists and ML Engineers to streamline experimentation and deployment.
• Help teams move ML solutions into production quickly and reliably.
• Balance speed of execution with long-term maintainability.
• Contribute to technical direction and best practices across the ML platform.

What We're looking For

Experience

• 5+ years of experience in:
  • Machine Learning Engineering
  • ML Platform Engineering
  • MLOps
  • Data Infrastructure
  • Distributed Systems
• Proven track record building and operating production systems.
• Experience supporting large-scale, high-traffic applications.
• Experience working with cloud infrastructure and modern development workflows.

Technical Skills

• Strong understanding of the machine learning lifecycle, including:
  • Feature engineering
  • Model training
  • Deployment
  • Inference
  • Monitoring
• Experience operating scalable distributed systems.
• Strong software engineering fundamentals.
• Understanding of reliability, observability and infrastructure operations.
• Experience building reusable platforms and developer tooling.

Infrastructure & Operations

• Experience with:
  • Containerisation
  • Infrastructure as Code
  • CI/CD pipelines
  • Monitoring and alerting systems
  • Cloud-native architectures
• Strong operational mindset and production troubleshooting skills.

Personal Attributes

• Strong ownership mentality.
• Comfortable operating independently.
• Excellent communication and collaboration skills.
• Pragmatic decision-maker who balances speed with quality.
• Enjoys working in highly collaborative environments.

Relevant Technology Experience

Experience with some or all of the following would be beneficial:

Data & Compute

• Python
• Spark
• Databricks
• Ray

Serving & ML Infrastructure

• Redis
• Tecton
• Ray Serve
• FastAPI
• Triton Inference Server

Cloud & Infrastructure

• AWS
• Google Cloud Platform (GCP)
• Docker
• Kubernetes
• Terraform

CI/CD & Operations

• GitHub Actions
• Jenkins
• Grafana
• Prometheus
• MLflow
• Weights & Biases (W&B)

Machine Learning Ecosystem

• PyTorch
• TensorFlow
• ONNX

Success In This Role

• ML workflows are automated, reliable and reusable across teams.
• Training, deployment and serving systems operate efficiently at scale.
• Production ML services maintain high availability and low latency.
• Teams can move quickly from experimentation to production.
• Infrastructure supports future growth without compromising reliability.
• Platform capabilities reduce operational overhead and increase engineering velocity.

Why Join?

• Work on machine learning infrastructure at massive scale.
• Build systems supporting hundreds of millions of players worldwide.
• Collaborate with leading ML engineers, data scientists and platform specialists.
• Solve challenging problems across MLOps, infrastructure and real-time serving.
• Significant technical ownership and impact.
• Opportunity to shape the future of machine learning platforms in one of gaming's most innovative environments.

Location: Helsinki, Finland (On Site)
Start Date: ASAP
Contract Duration: 3 Months (with possible extension)
Team: Machine Learning & Data Science
Day Rate:Up to €600 EUR per day

If you're a Machine Learning Engineer who loves building reliable, scalable ML infrastructure and enabling data science teams to move faster, we'd love to hear from you.