Senior Machine Learning Engineer

Hybrus AB; Sweden; Hybrid; SEK 600,000 - 800,000; Full time

Job summary

Hybrus AB is seeking a Senior ML Engineer for a long-term assignment in Stockholm. In this role, you will lead the implementation of Federated Learning projects, optimizing ML models for mobile platforms to enhance privacy and customer experience.

The ideal candidate has 5+ years of experience in ML Engineering, expertise in Federated Learning frameworks, and a strong background in deploying models on mobile devices. This position offers potential hybrid work options.

Qualifications

• 5+ years of experience in Machine Learning Engineering or related fields.
• Hands-on experience with Federated Learning frameworks.
• Strong understanding of model training and evaluation techniques.
• Experience deploying ML models on mobile devices.
• Ability to communicate complex technical concepts effectively.

Responsibilities

• Lead technical evaluation and implementation of Federated Learning initiatives.
• Work with teams to design end-to-end FL workflows.
• Define and execute experimentation plans for FL.
• Develop language models and on-device training pipelines.
• Shape roadmap for scaling FL from experimentation to production.

Skills

Machine Learning Engineering

Federated Learning frameworks

TensorFlow

Mobile deployment

NLP

Tools

TensorFlow Lite

PyTorch

FedML

OpenFL

Job description

We are looking for a Senior ML Engineer for our client in Stockholm for a long term assignment.

Employment type: Fixed Term Contract

Duration: 6 Months - 1 Year

Location: Stockholm

Work type: Onsite (potential hybrid options)

As a Senior ML Engineer, you will be working hands‑on to optimise training and deployment of ML models to be quick and cost‑efficient. You will also be at the forefront of putting our ML models on mobile devices to enhance data privacy and customer experience. To achieve this, you will need to collaborate with teams to establish best practices and tools for efficient ML model development and deployment, particularly on mobile platforms. You will be expected to help the client reach and stay at the cutting edge of ML training and deployment, as well as explore new frontiers such as federated learning.

Responsibilities

• Lead the technical evaluation and implementation of Federated Learning (FL) initiatives.
• Work closely with Data Science, Android, and Backend teams to design and validate end‑to‑end FL workflows.
• Define and execute experimentation plans to assess the effectiveness of FL for use cases.
• Develop and optimize language models and on‑device training pipelines for privacy‑preserving machine learning.
• Establish model evaluation frameworks, success metrics, and validation strategies for FL‑based systems.
• Identify technical risks, assumptions, and limitations, and provide recommendations on architecture and future direction.
• Help shape the roadmap for scaling FL from experimentation to production‑ready systems.

Qualifications

• 5+ years of experience in Machine Learning Engineering, Applied Machine Learning, or related fields.
• Hands‑on experience with Federated Learning frameworks such as TensorFlow Federated, Flower, FedML, OpenFL, or equivalent.
• Strong understanding of distributed machine learning, model training, and model evaluation techniques.
• Experience working with NLP, language models, embeddings, or text classification systems.
• Hands‑on experience deploying ML models on mobile devices (e.g., TensorFlow Lite, Core ML, ONNX Runtime Mobile).
• Strong knowledge of machine learning frameworks such as TensorFlow and PyTorch.
• Experience designing and executing ML experiments, analyzing results, and driving data‑driven decisions.
• Familiarity with privacy‑preserving machine learning concepts and challenges.
• Ability to work across multiple teams and communicate complex technical concepts to both technical and non‑technical stakeholders.
• Strong problem‑solving skills and ability to operate in an exploratory research and PoC environment.

Desired skills

• Experience deploying or operating Federated Learning systems in production environments.
• Hands‑on experience with on‑device machine learning technologies such as TensorFlow Lite, ONNX Runtime Mobile, or Core ML.
• Experience building machine learning solutions for mobile applications.
• Experience in messaging, spam detection, fraud detection, trust & safety, or similar domains.
• Familiarity with the challenges of running ML workloads on mobile devices.
• Experience with MLOps, model monitoring, and automated training/deployment pipelines.