Asks for

The ad says“Strong understanding of data structures, algorithms, and software engineering best practices.”
The ad says“Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.”
The ad says“Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.”
The ad says“Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.”
The ad says“Strong understanding of data structures, algorithms, and software engineering best practices.”
The ad says“Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.”
The ad says“Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.”
The ad says“Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.”
The ad says“Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain”
The ad says“Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain”
The ad says“Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain”
The ad says“Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.”
The ad says“Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.”
The ad says“Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.”
The ad says“Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain”
The ad says“Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).”
The ad says“Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).”
The ad says“Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain”
The ad says“Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).”
The ad says“Strong understanding of data structures, algorithms, and software engineering best practices.”
The ad says“Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).”
The ad says“Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.”

About ASI

ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible.

What you will do:

As part of our core engineering team, you will design and deploy production-grade systems that integrate machine learning models into scalable software pipelines. You’ll develop and ship features that leverage ML to solve real-world optimization and prediction problems, working with modern infrastructure like Kubernetes, AWS, and MLOps tooling. You’ll approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale.

What we value:

Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain
Strong understanding of data structures, algorithms, and software engineering best practices.
Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.
Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.
Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.
A bias for simplicity and clarity in solving complex problems.
Intellectual curiosity and willingness to collaborate.
Clear communication and collaboration across cross-functional teams.

What we offer:

Flexible Working Hours: With a global team supporting mission-critical operations, we have to be at our best, so we’ve adopted flexible hours to allow for balance.
Premium Healthcare and Health Insurance: Health and wellness are critical to living a happy and resilient life. We provide first-class medical, dental and vision coverage to you and your dependents.
Competitive Salary & Equity: Our team is our biggest asset. We value the hard work that each person commits to us, so we provide competitive, transparent compensation packages.
Generous relocation package to assure smooth relocation to Tricity area.
High Energy Environment: We live by the mantra that now is better than never. You will find yourself surrounded by peers who constantly challenge the status quo.
Flexible Time Off: We encourage you to take time off as you need it. While our team is hard-working, our success is measured by output—not time spent.
Office, Equipment & Tools: We bring the best tools to the mission, from ergonomic desk setups to modern productivity software. We have a freshly prepared team breakfast and lunch every day.

How do we hire:

We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you.