Senior Applied Research Engineer - Optimization, Simulation & ML (python/go)

Unlike other companies, being a Senior Engineer isn’t just a paygrade or empty title for us here at akeno. It’s a distinction! It means you have built successful products in the past, faced mysterious production errors and came out on top of them, know how to get stuff done right, not just fast.

Being a Senior Applied Research Engineer means you can see a features implications for the database, API and related services - even for complex topics like scheduling and other NP-hard problems.

You’ll be working in our AI & Optimization Team with team mates that “know their stuff”. Colleagues who are there when you need them and not afraid to ask when you can help them. We don’t have dedicated architects or tech leads because frankly—if you’re a true senior—you should be capable of creating a great first draft yourself.

Additionally you will have Full Stack colleagues doing the heavy lifting to implement the algorithms and logics you build into our application.

You will be working at the heart of our application. We are rapidly changing and evolving our core logics and algorithms to achieve ever lasting positive impact on the performance of our customers supply chains. The backend is a hybrid combination of constraint programming, adaptive heuristics as well as machine learning to find the fastest and best solution for the factories of our customers.

You will be facing complex requirements in a complex domain - which is inherently not mathematically end-to-end solvable. So if you are keen to find scalable, generic solutions for complex mathematical problems - you have found your match!

What you will be doing

• Build heuristic/ rule based algorithms enabling high speed solution discovery
• Build mathematical solvers based on google's ortools
• Maintain our simulation engine to evaluate solutions/ schedules
• Work on our core data model (graphs) representing our solution states
• Work on meta-optimizations which optimize the heuristic solvers compositions and parameters
• Optionally: Collaborate on ML implementations for pattern recognition of prior solutions using GNNs and tree classifiers

• Extensive knowledge and expertise in coding in python
• Capability to code on senior level in at least Rust or Golang
• Mathematical understanding of optimization for scheduling problems
• Profound understanding and experience working with graph data
• Experience building long-term sustainable software end-to-end
• Capability to work independently on subtopics
• Capability to work in large and complex codebases

To learn more about our 3-step recruiting process and check out our Tech Radar, visit akeno.ai