{"id":105892,"date":"2026-08-05T06:22:12","date_gmt":"2026-08-05T06:22:12","guid":{"rendered":"https:\/\/jobs.dataaxisnode.com\/kenya\/uncategorized\/senior-ai-ml-engineer\/"},"modified":"2026-08-05T06:22:12","modified_gmt":"2026-08-05T06:22:12","slug":"senior-ai-ml-engineer","status":"publish","type":"post","link":"https:\/\/jobs.dataaxisnode.com\/kenya\/jobs\/nairobi\/senior-ai-ml-engineer\/","title":{"rendered":"Senior AI\/ML Engineer"},"content":{"rendered":"<p><script type=\"application\/ld+json\">{\"@context\": \"https:\/\/schema.org\/\", \"@type\": \"JobPosting\", \"title\": \"Senior AI\/ML Engineer\", \"description\": \"Role Summary\\nProject A is ALX\u2019s AI learning platform. Under it sits a competency model and the hard algorithmic question of the whole system: given what we know about a learner, what should they do next? The Senior AI\/ML Engineer designs and builds that engine. It is a probabilistic path-recommendation problem in the family of Bayesian Knowledge Tracing and Knowledge Space Theory, and it has to work from a cold start: no behavioural data yet, so the model is the prior. You encode the prerequisite structure of the domain and let it update as learners come through. We deliberately want an engineer with real modelling depth rather than a pure data scientist on a team this small, the high-value work on day zero is building, not analysing data we don\u2019t yet have.\\nSpecific Responsibilities\\nCompetency Navigation Algorithm\\nOwn the competency navigation algorithm behind the Learner-Competency-Mapper, its design, implementation, and update dynamics as real data arrives.\\nEncode the prerequisite structure of the domain and its priors, so the model performs from a cold start and improves as learners flow through.\\nLLM Processing & ML Growth\\nBuild pipelines that turn unstructured platform data into signal -\u00a0 first, a constrained LLM-as-judge answering whether Chidi is effective, with model selection, eval design, and awareness of judges\u2019 own failure modes.\\nAs the platform accumulates a feedback stream, builds behavioural and at-risk profiling and the models that evaluate learners for the Grader-Competency-Pulser; the role grows into genuine ML\/data-science work as the data asset does.\\nSkill Requirements - Essential\\nProbabilistic \/ Bayesian modelling: real depth \u2014 you have designed models from domain structure, not just fit them to data.\\nPython & shipping: strong Python and the ability to ship what you design, production pipelines, not notebooks.\\nEvaluation: eval design experience, or the judgment to build it fast.\\nDesirable not required: BKT\/KST or psychometrics exposure; MLflow or similar experiment tracking; knowledge graphs. No prior EdTech required but useful.\\nSerious probabilistic modelling of structured domains recommenders, knowledge graphs, causal inference is great.\\nEssential Traits for Success\\nYou reason carefully about your assumptions \u2014 in a cold-start model, bad priors compound silently, and you find that problem interesting.\\nYou learn unfamiliar domains fast and enjoy it.\\nYou can talk about a model that was wrong and how you found out.\", \"datePosted\": \"2026-08-04\", \"hiringOrganization\": {\"@type\": \"Organization\", \"name\": \"ALX\"}, \"jobLocation\": {\"@type\": \"Place\", \"address\": {\"@type\": \"PostalAddress\", \"addressLocality\": \"Nairobi\", \"addressCountry\": \"KE\"}}, \"directApply\": true, \"validThrough\": \"2026-08-18T23:59:59\", \"employmentType\": \"FULL_TIME\"}<\/script><\/p>\n<p><strong>Company:<\/strong> ALX<\/p>\n<p><strong>Location:<\/strong> Nairobi<\/p>\n<p><strong>Job Type:<\/strong> Full Time , Remote<\/p>\n<p><strong>Apply Before:<\/strong> 2026-08-18<\/p>\n<h3>Job Description<\/h3>\n<p>Role Summary<br \/>Project A is ALX\u2019s AI learning platform. Under it sits a competency model and the hard algorithmic question of the whole system: given what we know about a learner, what should they do next? The Senior AI\/ML Engineer designs and builds that engine. It is a probabilistic path-recommendation problem in the family of Bayesian Knowledge Tracing and Knowledge Space Theory, and it has to work from a cold start: no behavioural data yet, so the model is the prior. You encode the prerequisite structure of the domain and let it update as learners come through. We deliberately want an engineer with real modelling depth rather than a pure data scientist on a team this small, the high-value work on day zero is building, not analysing data we don\u2019t yet have.<br \/>Specific Responsibilities<br \/>Competency Navigation Algorithm<br \/>Own the competency navigation algorithm behind the Learner-Competency-Mapper, its design, implementation, and update dynamics as real data arrives.<br \/>Encode the prerequisite structure of the domain and its priors, so the model performs from a cold start and improves as learners flow through.<br \/>LLM Processing &#038; ML Growth<br \/>Build pipelines that turn unstructured platform data into signal &#8211;\u00a0 first, a constrained LLM-as-judge answering whether Chidi is effective, with model selection, eval design, and awareness of judges\u2019 own failure modes.<br \/>As the platform accumulates a feedback stream, builds behavioural and at-risk profiling and the models that evaluate learners for the Grader-Competency-Pulser; the role grows into genuine ML\/data-science work as the data asset does.<br \/>Skill Requirements &#8211; Essential<br \/>Probabilistic \/ Bayesian modelling: real depth \u2014 you have designed models from domain structure, not just fit them to data.<br \/>Python &#038; shipping: strong Python and the ability to ship what you design, production pipelines, not notebooks.<br \/>Evaluation: eval design experience, or the judgment to build it fast.<br \/>Desirable not required: BKT\/KST or psychometrics exposure; MLflow or similar experiment tracking; knowledge graphs. No prior EdTech required but useful.<br \/>Serious probabilistic modelling of structured domains recommenders, knowledge graphs, causal inference is great.<br \/>Essential Traits for Success<br \/>You reason carefully about your assumptions \u2014 in a cold-start model, bad priors compound silently, and you find that problem interesting.<br \/>You learn unfamiliar domains fast and enjoy it.<br \/>You can talk about a model that was wrong and how you found out.<\/p>\n<h3>How to Apply<\/h3>\n<p>Interested and qualified? Go to ALX on job-boards.greenhouse.io to apply<br \/>Build your CV for free. Download in different templates.<\/p>\n<p><a href=\"https:\/\/www.myjobmag.co.ke\/apply-now\/1296507\" target=\"_blank\" rel=\"noopener\" style=\"display:inline-block;padding:10px 20px;background:#2271b1;color:#fff;text-decoration:none;border-radius:4px;\">Apply Now<\/a><\/p>\n<p><small>Source: MyJobMag<\/small><\/p>\n<p><!-- job-expiry: 2026-08-18 --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Company: ALX Location: Nairobi Job Type: Full Time , Remote Apply Before: 2026-08-18 Job Description Role SummaryProject A is ALX\u2019s AI learning platform. Under it sits a competency model and the hard algorithmic question of the whole system: given what we know about a learner, what should they do next? The Senior AI\/ML Engineer designs [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1408,1409],"tags":[1944,1504,1413,1412],"class_list":["post-105892","post","type-post","status-publish","format-standard","hentry","category-jobs","category-nairobi","tag-alx","tag-full-time-remote","tag-job-listing","tag-myjobmag"],"_links":{"self":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts\/105892","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/comments?post=105892"}],"version-history":[{"count":0,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts\/105892\/revisions"}],"wp:attachment":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/media?parent=105892"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/categories?post=105892"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/tags?post=105892"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}