Interested in this AI/ML Engineer role at phygital grp?
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About the role
Location: Remote · Type: Full-time or contract · Reports to: James Hattem, Founder & CEO
Phygital Group is looking for an AI Creative Producer who can turn a brief into finished, scroll-stopping content — AI video, lifelike AI humans, talking-head videos, product visuals and design — at agency speed and brand-level quality.
Phygital Group is a full-service digital marketing and brand growth agency based in Québec. We run social media, paid ads, content production, influencer marketing and web development for brands across beverage, beauty, hospitality, fashion, real estate and CPG. You'll work across that whole roster, so no two weeks look the same.
What you'll do
- AI video creation — produce short-form and campaign videos with AI generation tools, from concept to final cut.
- Realistic AI humans — create and recreate people with AI so they look natural and consistent across shots, faces, hands, lighting and motion.
- Talking videos — build AI spokesperson and avatar videos with accurate lip-sync, natural voice and believable expressions.
- Product integration — place client products into AI scenes as exact replicas: correct label, logo, colour, shape and proportions, every time.
- Dynamic editing — cut fast, high-retention edits for Reels, TikTok and ads: pacing, transitions, motion graphics, captions, sound design and colour.
- Photo work — retouch, composite and create product and lifestyle images in Photoshop and AI image tools.
- Vision boards & creative direction — build mood boards and visual concepts that align clients and the team before production starts.
- Graphic design — design social posts, ad creatives, thumbnails and brand assets that match each client's identity.
What you bring
- A portfolio that shows photorealistic AI humans and AI video — work people can't tell was AI-generated.
- Proven skill making AI products look identical to the real thing (packaging, labels, logos).
- Strong video editing with a sharp sense of pacing, hooks and trends for social and paid ads.
- Expert Photoshop skills: retouching, compositing, colour matching, clean masking.
- A designer's eye for layout, typography and brand consistency.
- Hands-on experience with current AI tools, for example:
o Video: Runway, Kling, Veo, Sora, Luma, Higgsfield
o Images: Midjourney, Flux, Nano Banana, Photoshop Generative Fill
o Talking avatars and voice: HeyGen, Hedra, ElevenLabs
o Workflows: ComfyUI, LoRA or custom model training
- Editing and design software: Premiere Pro or DaVinci Resolve, After Effects, CapCut, Photoshop, Illustrator, Figma or Canva.
- Fast turnaround, organized files, and comfort with feedback and revisions.
- Clear communication in English (French is a plus) and reliable availability during Eastern Time business hours.
Nice to have
- 3D or CGI product rendering (Blender, Cinema 4D)
- Motion graphics and animated typography
- Agency experience with multiple brands and tight deadlines
- Experience with UGC-style ads and influencer content
What we offer
- 100% remote, flexible schedule
- Work on real campaigns for brands in beverage, beauty, hospitality, fashion and entertainment
- Creative freedom to test new AI tools and workflows
- Room to grow into a lead creative / head of AI content role
- Compensation: to be confirmed based on experience
How to apply
Send the following to [email protected] with the subject line "AI Creative Producer – Your Name":
- Your portfolio or reel (AI video, AI humans, talking videos, product work)
- 2–3 examples where you recreated a real product or person with AI, with the original reference beside it
- The AI tools and software you use daily
Pay: $72,148.88 - $86,888.97 per year
Work Location: Remote
Salary Context
This $72K-$86K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At phygital grp, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($79K) sits 62% below the category median. Disclosed range: $72K to $86K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
phygital grp AI Hiring
phygital grp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $86K - $86K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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