Remote job · Business and finance
Staff Machine Learning and Operations Research Engineer
In one line: Staff Machine Learning and Operations Research Engineer is a remote role in Canada at a logistics technology scale-up.
- Remote (Canada)
- A logistics technology scale-up
- Posted 7 Oct 2026
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https://keywordise.com/mcpApply to Keywordise job veyr5bsm7bAbout the role
A hands-on senior engineer defines the machine learning and optimization direction for a dispatch platform, shaping how deliveries are priced, forecast, selected and routed. The work has company-wide impact on revenue, margin and reliability.
At a glance
- PayNot stated in the posting
- Remote scopeRemote, Canada
- Posted7 Oct 2026
- Last checked18 Oct 2026
What you will do
- Own the machine learning and optimization roadmap, deciding which problems use models, solvers or heuristics
- Lead the most ambiguous, high-stakes modeling problems from framing through to production
- Set standards for experimentation, evaluation and production machine learning, and mentor engineers
- Partner with product and leadership to shape strategy and find competitive advantage
What they look for
- Hands-on experience building and shipping machine learning models and optimization systems at scale
- Ability to set technical direction and make architectural decisions across a platform
- Strong grounding in experimentation, model evaluation and production machine learning practices
- Experience with solver-based optimization tools such as OR-Tools or Gurobi
- Comfort leading ambiguous problems and guiding work across engineering and data teams
Questions about this job
Is this job fully remote, and from where?
Yes, it is listed as remote, aimed at candidates in Canada.
What does this job pay?
The posting does not state a salary, so there is no figure to quote.
What experience is needed?
The posting asks for: hands-on experience building and shipping machine learning models and optimization systems at scale; ability to set technical direction and make architectural decisions across a platform; strong grounding in experimentation, model evaluation and production machine learning practices.
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