Machine Learning Search Engineer

Diverse Lynx
Birmingham
3 days ago
Create job alert
Overview

Role: Machine Learning Search Engineer

Location: Fully Remote, Must be in Atlanta, GA or Birmingham, AL

Duration: Full Time

Experience: 8+ Year

Responsibilities
  • Build, maintain, and operate Python based ML pipelines for embeddings, inference, and relevance ranking
  • Develop and support vector search and similarity matching to enable intent based product discovery
  • Support GPU based workloads for model training, computation, and inference
  • Participate in end to end MLOps workflows, including deployment, monitoring, retraining, and system maintenance
  • Manage and refresh embeddings as product data and catalogs evolve over time
  • Collaborate closely with Innovation, Architecture, and Engineering teams to deliver scalable ML systems
  • Debug, optimize, and improve the performance, reliability, and relevance of search pipelines
  • Contribute to ongoing platform enhancements as the search ecosystem matures
Qualifications
  • Strong Python development experience (primary language)
  • Experience building, deploying, or supporting machine learning pipelines in production
  • Solid understanding of the ML lifecycle, including training, inference, retraining, and monitoring
  • Familiarity with MLOps principles and production ML systems
  • Experience working with large datasets and model outputs
  • Ability to work hands on in evolving systems with ambiguous or rapidly changing requirements

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer – Founding Team (Computer Vision / GenAI)

Data Scientist (Semantic Search/Recommender Systems)

Senior Machine Learning Scientist - Search

Staff Machine Learning Engineer

Machine Learning Engineer

Machine Learning Specialist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.