Machine Learning Engineer, Video

Cantina
London
10 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Principal Machine Learning Engineer

Lead Machine Learning Engineer

Machine Learning Engineering Lead

Senior Data Scientist SME & AI Architect

Applied Machine Learning Lead

A bit about Cantina:

Cantina, founded by Sean Parker, is a new social platform with the most advanced AI character creator. Build, share, and interact with AI bots and your friends directly in the Cantina or across the internet.

Cantina bots are lifelike, social creatures, capable of interacting wherever humans go on the internet. Recreate yourself using powerful AI, imagine someone new, or choose from thousands of existing characters. Bots are a new media type that offer a way for creators to share infinitely scalable and personalized content experiences combined with seamless group chat across voice, video, and text.

If youre excited about the potential AI has to shape human creativity and social interactions, join us in building the future!

A bit about the role:

We are looking for Research Engineers to contribute to the development of state-of-the-art AI systems for real-world applications, focusing on real-time and offline human-centric video generation (currently focusing on talking heads). Our technology leverages diffusion models, flow matching, and GANs. We are also working on network optimization and audio-driven avatars.

As a Research Engineer, you will work on:

  1. Data processing, cleaning, and filtering to ensure high-quality datasets for training and experimentation.
  2. Optimizing and accelerating pipelines to improve performance and efficiency.
  3. Testing hypotheses and building prototypes for new features or model improvements.
  4. Collaborating with researchers and engineers to integrate prototypes into production pipelines.
  5. Supporting the team in deploying and scaling AI solutions.

Requirements:

  1. Experience in data processing and creating efficient pipelines for large-scale datasets.
  2. Strong programming skills in Python, with expertise in frameworks like PyTorch.
  3. Knowledge of image/video generation, audio sequence-to-sequence models, model optimization, and distillation (experience in all areas is not mandatory).
  4. Publications in reputable conferences or journals are a plus.

Why Join Cantina AI?

  1. Opportunity to work on groundbreaking AI technologies that redefine human-computer interaction.
  2. Collaborative and inclusive culture that values innovation, continuous learning, and professional growth.
  3. Competitive compensation package including equity options, healthcare benefits, and flexible work arrangements.

Please note:

  1. This is aremoterole from London, England, but we are also open to remote in the GMT time zone.
  2. The salary starts at $175-250k + stock option plan.
  3. This is full-time employment only -no contractors possible- usually through Remote.com.
  4. We are unable to sponsor location based visas.

Application Process:Please submit your resume, cover letter, and any relevant portfolio or publications demonstrating your research contributions in AI.

J-18808-Ljbffr

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.