Senior CV ML Engineer: PyTorch + Vision Transformers (Edge)

Nordic Investin Group Aktiebolag; Göteborgs kommun; On-site; SEK 1,100,000 - 1,400,000; Full time

Job summary

Nordic Investin Group Aktiebolag is recruiting a Senior Computer Vision Machine Learning Engineer to drive production CV systems from training to edge inference. You will align architecture, delivery and operations, translating constraints into PyTorch-based solutions and robust data pipelines.

You will work with engineers, analysts and domain experts, ensuring measurable outcomes, clear trade-offs and sustainable ownership across the team, with emphasis on operability and responsible deployment.

Qualifications

• Experience delivering production computer-vision systems from training to edge inference.
• Strong knowledge of PyTorch and vision transformers.
• Experience with data pipelines and model optimisation.
• Ability to assess risk, priorities and trade-offs for edge deployment.
• Evidence of clear stakeholder communication and end-to-end ownership.

Responsibilities

• Own the practical direction for production computer-vision systems from training to edge inference.
• Translate goals and constraints into a clear approach for PyTorch and vision transformers.
• Establish useful standards, routines or artefacts around data pipelines.
• Coordinate stakeholders and dependencies affecting model optimisation.
• Use evidence and measurable outcomes to improve edge deployment.
• Strengthen knowledge sharing, decision quality and sustainable ownership across the team.

Skills

PyTorch

Vision transformers

Data pipelines

Stakeholder communication

End-to-end ownership

English language

Tools

PyTorch

Job description

Nordic Investin Group Aktiebolag is recruiting a Senior Computer Vision Machine Learning Engineer to drive production CV systems from training to edge inference. You will align architecture, delivery and operations, translating constraints into PyTorch-based solutions and robust data pipelines.

You will work with engineers, analysts and domain experts, ensuring measurable outcomes, clear trade-offs and sustainable ownership across the team, with emphasis on operability and responsible deployment.