39 positions available in Stockholm. Sweden’s largest market — services, finance, public sector, engineering, and a strong tech scene
OrbDB Labs AB • Stockholm, Stockholms län
Ictech • Stockholm, SE
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Get startedIctech • Stockholm, SE
Hedvig • Stockholm, Stockholm, SE
Game Boost - Leveling Up Game Production Everywhere • Stockholm, Sweden, SE
Modulai • Stockholm, Sweden, SE
ManoMotion • Stockholm, SE
RebTel Networks AB • Stockholm, Stockholms län
Nextory AB • Stockholm, Stockholms län
Rebtel • Stockholm, Sweden, SE
Rebtel • Stockholm, Sweden, SE
RebTel Networks AB • Stockholm, Stockholms län
Knowit Aktiebolag (publ) • Stockholm, Stockholms län
Ants • Stockholm, SE
Ants • Stockholm, SE
Ants • Stockholm, SE
Advania • Stockholm, Sverige, SE
TechSeed • Stockholm, Stockholm, SE
Rebtel • Stockholm, Sweden, SE
NEXER TECH TALENT • Stockholm, SE
Avalanchestudios • Stockholm
Explore employers on the map — Stockholm
AI/ML engineers develop machine learning models and AI systems that learn from data to make predictions or decisions. They work on model architecture, training, optimization, and deployment to production systems.
The median salary for a AI / ML Engineer in Stockholm is SEK 674,400 per year. The range is SEK 572K - SEK 800K (P10: SEK 492,000, P90: SEK 933,600). (SCB 2025) Typical US salary range: $110K - $180K. In Europe: €75K - €120K. Cost of living in Stockholm is high compared to other cities.
Sweden’s largest market — services, finance, public sector, engineering, and a strong tech scene. Sweden's largest market, hiring across services, finance, the public sector, engineering, and tech.
Top industries: Services & Finance, Public Sector, Engineering, Technology.
Excellent public transit and cycling infrastructure; many companies offer flexible remote policies.
AI/ML engineers advance to senior ML engineer, research engineer, or ML platform architect; many pursue specialization in specific domains like NLP or computer vision. AI/ML engineering in 2026 is dominated by generative AI, with massive demand for engineers who can fine-tune and deploy LLMs in production environments.
Walk through your model development lifecycle—discuss data preparation, model selection, hyperparameter tuning, and evaluation metrics that matter for the business.
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