Interested in this AI/ML Engineer role at Aventix?
Apply Now →Skills & Technologies
About This Role
Head of AI
Lead AI at a mission-driven bioanalytics company, and build the team that delivers it.
Gothenburg, Sweden (Medicinaregatan 8B) · Full-time, permanent · Start by agreement
Our mission
We are on a mission to change the world by democratizing bioanalytics in the life sciences. Trustworthy measurement shouldn’t be a privilege of the best-equipped labs. Today, scientists everywhere lose samples, assays and weeks of work to errors nobody saw happen. We think that is fixable.
Aventix builds sensor-based bioanalytical tools that show labs what actually happened in their processes. Our first product, Scarlett™ 01 , verifies dispensed volumes and flags problems before they become lost samples, failed assays or reruns. We already work with labs in pharma, chemical and agricultural R&D through our Early Access Program. Scarlett is the start of a platform for trustworthy lab measurement , and we intend to lead the lab tools industry while building it.
Missionaries, not mercenaries
We are a small and efficient team of hard-core doers. We are not looking for someone who wants a job; we want someone who wants this job. If you are optimizing for title, perks or a quiet life, there are better places for you. If you want your work to matter, read on.
• Purpose first. Every model and pipeline should move a real lab closer to data it can trust.
• Bias for action. We ship, measure, learn and ship again. A working prototype on real data beats a perfect plan.
• Ownership. Nobody says “not my job.” You see a problem, you own it until it is solved.
• Data over ego. We build measurement tools, so the evidence wins the argument.
The role
As Head of AI, you will own Aventix’s AI strategy and how raw sensor data becomes reliable models, insights and customer-facing capabilities. Working closely with our CTO and founders, you will set the direction, build the team and shape Aventix as a company, not just its models.
This is a hands-on leadership role. In your first year, you will set the strategy and still write code and ship models yourself, side by side with our scientists, engineers and customers.
What you will do
- Own the AI roadmap and connect it to our product and platform vision
- Decide what we build, buy or leave alone, and set standards for data, validation and model governance
- Hire, lead and mentor the first ML and data team members
- Lead the architecture and build-out of our ML and data infrastructure
- Develop, test and improve models on real measurement data
- Build pipelines that collect, process and analyse that data
- Tackle segmentation, anomaly detection, signal processing and pattern recognition
- Take models from prototype to reliable, production-ready features
- Work with Early Access customers to turn their needs into product, and represent our AI to customers, partners and investors
What you bring
- Experience leading ML work from idea to production, ideally in a startup or new product
- A track record of hiring, mentoring or leading engineers or data scientists
- Strong Python and hands-on machine learning skills
- Solid grounding in statistics, data analysis and model validation
- A track record of developing and evaluating models on messy, real-world data
- Good software engineering habits and data-processing skills
- The ability to work independently and across disciplines
- Clear communication in English of AI trade-offs to founders, customers and investors
Bonus points
- Time-series, anomaly detection, signal processing or deep learning
- Scientific, experimental or measurement data
- ML deployment, MLOps, monitoring or data processing at scale
- Laboratory tools, life sciences or a related field
- A PhD degree, Master’s degree or equivalent experience in ML, CS, engineering, physics, mathematics or a related field
Who you are
You believe in the mission and want to look back knowing you helped change how science gets done. You are a leader who still loves to build, and a doer who would rather ship something real this week than debate it for a month. You are relentlessly curious about where AI is going: you experiment, build your own ideas and keep learning because you enjoy it. Here, you will point that energy at real measurement data, real lab problems and technologies still being built.
What we offer
Competitive salary and equity stake that reflects the seniority of the role, pension and insurance per Swedish standards, 30 days of annual leave, a hardware and compute budget that matches the work, conference support, and visa sponsorship for the right candidate.
Interview process
We move fast. We aim to finish within three weeks of first contact.
- Intro call with our CTO (30 min)
- Technical deep dive into your past work, topic of your choice (60–90 min)
- Your first 90 days: how you would set up AI at Aventix, tested on a real sensor-data problem. Discussion-based, no gotcha problems.
- Vision and mission conversation with our Founders.
- On-site in Gothenburg: meet the team, see Scarlett run, talk product and roadmap
- Offer
Apply
Send your CV, a GitHub, portfolio or publication list if you have one, and a few sentences on the messiest sensor data you have ever had to make sense of to [email protected] , subject Head of AI . No cover letter needed. Tell us why this mission matters to you. We read everything.
Aventix is an equal opportunity employer. We hire on capability, curiosity and commitment to the mission, and welcome applicants from every background. Unsure if you qualify? Apply anyway.
Precision for every lab. Progress for mankind.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Aventix, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Aventix AI Hiring
Aventix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Gothenburg, Västra Götaland County, Sweden.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.