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About The Company A VC-backed AI/ML startup in West London building a novel foundation model for fully automated, unsupervised software delivery in embedded control systems. Early stage, high urgency, high transparency. They value directness over jargon and hands-on ownership over titles. What You'll Actually Do Own a large-scale foundation model end to end, from research through production. This is 0 to 1 work, not maintaining someone else's architecture. Design and implement custom CUDA kernels where off-the-shelf libraries fall short. Architect and scale distributed training and inference pipelines on cloud infrastructure. Build and operate ML systems with strict production SLOs. Your models ship, not just train. Create internal tooling and infrastructure that accelerates the whole team's output. Work in a fast, ambiguous environment where you define what needs building next. Core stack: CUDA, C++, Python, PyTorch, distributed training, GPU infrastructure, foundation models Criteria I check in every CV (must-haves, only these matter) You've shipped a large-scale foundation model from 0 to 1 at a high-growth AI/ML startup or a top-tier research lab. Papers alone don't count. You've designed and implemented custom CUDA kernels to optimize model performance. This is non-negotiable. You have direct hands-on experience scaling distributed training or inference pipelines on cloud infrastructure (AWS, GCP, or Azure). You've owned an ML system with strict production SLOs or SLAs end to end, not just a research prototype. What Gets Rejected Immediately Academic or research-only experience without commercial production delivery. No proficiency in CUDA C/C++ or Python. No distributed training or inference experience at scale. EU AI Act compliance I use TeamTailor's built-in Co-Pilot to extract signals from CVs during the review stage. The decision to move a candidate forward is always made by a human person (me or the client). No automated decisions are made about your application.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.