Senior ML Engineer

Hybrus AB; Stockholms kommun; Hybrid; SEK 837,000 - 1,116,000; Full time

Job summary

Hybrus AB is seeking a Senior ML Engineer for a long-term project in Stockholm. In this role, you will optimise training and deployment of ML models, focusing on mobile integration to enhance data privacy. You will collaborate with various teams to implement best practices in ML model development and deployment, particularly in Federated Learning.

The position requires strong hands-on experience and a deep understanding of machine learning frameworks. This role offers a dynamic work environment aimed at making significant impacts in the field.

Qualifications

• 5+ years of experience in Machine Learning Engineering or related fields.
• Hands-on experience with Federated Learning frameworks.
• Strong problem-solving skills in exploratory research environments.

Responsibilities

• Lead the technical evaluation of Federated Learning initiatives.
• Collaborate with Data Science and Mobile teams to design workflows.
• Develop on-device training pipelines for privacy-preserving ML.

Skills

Machine Learning Engineering

Federated Learning frameworks

Distributed machine learning

On-device machine learning

NLP and language models

Tools

TensorFlow

PyTorch

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 Client reach and stay at the cutting edge of ML training and deployment, as well as explore new frontiers such as federated learning.

The impact you will create

• 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 optimise 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.

What you bring in

• 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, analysing 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.

It would be great if you also have

• 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.