Interested in this AI/ML Engineer role at Vention?
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### Company Description
Headquartered in Montreal, Detroit and Munich, Vention helps manufacturers automate their operations in record time with the only hardware and software AI\-powered platform built for the factory floor. Our technology powers over 25,000 machines across 4,000 factories across 5 continents and we have the privilege to work with a significant proportion of Fortune 500 manufacturers, from space rockets, to electrical cars, to robotics.
At Vention, you’ll work alongside driven and talented people who care deeply about their craft and the impact they create. We’re a team of high achievers who grow through meaningful work, solving complex challenges, learning fast, and seeing the results of our efforts every day.
We move quickly and aim high, but we do it together with care, collaboration, and respect. Our culture celebrates diverse perspectives and supports your growth through intentional development, strong leadership, and opportunities to make a real difference.
### What You’ll Do
Manufacturing is at an inflection point. Labor shortages, production variability, and the arrival of production\-grade Physical AI are converging — and Vention is leading the charge. We launched Rapid Operator AI at NVIDIA GTC 2026, introduced GRIIP to the world, and now operate across 4,000 factories. What we need now is someone who can lead this area of opportunity to global success.
This is not a traditional sales role. You are a Physical AI authority embedded across the entire sales force — technically deep enough to architect solutions for a robotics engineer, commercially sharp enough to lead on strategic accounts, and credible enough to move a VP of Operations from curiosity to signed purchase order.
A DAY IN THIS ROLE
You start the morning on a video call with a process engineering team at a Tier 1 aerospace components manufacturer. They’ve had three failed attempts at automating a deep bin\-picking task with traditional vision systems. You walk them through GRIIP’s CAD\-to\-pick workflow, pulling up a live Rapid Operator AI demo and showing 99% first\-pick accuracy on parts that look almost identical to theirs. The robotics engineer is skeptical about occlusion handling in deep bins — you address it directly with technical specifics, then pivot to the payback model. The ops VP leans in when you show sub\-two\-year ROI on a two\-shift line. You leave with a proof\-of\-concept scoped and a next step on the calendar.
By mid\-morning you’re on Slack with three sales reps across different regions — one in Germany needs help positioning GRIIP against a competitor’s vision system for an industrial machinery account; another in the US wants qualification criteria for a CPG prospect who’s asking about lights\-out operation. You answer both in minutes — this is the muscle the playbook you’re building will eventually put in every rep’s hands without needing you in the room.
After lunch you join a call with Vention’s Head of Product and two product managers. You’ve been tracking a recurring objection in A\&D: customers want traceability logging at the grasp level for quality assurance. You’ve heard it in four deals this quarter. Today you’re presenting the pattern, the revenue at stake, and a proposed positioning response while the roadmap team assesses feasibility. This is the loop that makes the product better and your deals easier.
You end the day preparing for next week’s site visit to a CPG plant in the Midwest — a strategic account the regional rep has been working on for six months. You’re joining for the final technical evaluation. You review the plant layout, the parts they need to pick, and the competitive alternatives on the table. Tomorrow you board a flight. By Thursday you expect a purchase order.
Responsibilities
- Serve as the global subject\-matter authority on Vention’s Physical AI portfolio — Rapid Operator AI, GRIIP, MachineMotion AI, sensor\-to\-action policies, and the full\-stack platform as it applies to unstructured manufacturing tasks
- Work cross functionally from Customers to Physical AI Engineering to validate use case feasibility, develop tailored proposals and secure wins
- Support in\-house client feasibility assessments conducted pre\-sales on robotics test cells
- Work with and through quota\-carrying sales reps as an overlay specialist, accelerating their Physical AI pipeline and win rate across all regions and verticals
- Own named strategic accounts Physical AI opportunities where deal complexity, deal size, or vertical expertise warrants a specialist\-led commercial motion
- Deliver compelling technical demonstrations, proof\-of\-concept engagements, and ROI modelling that convert robotics engineers and C\-suite buyers alike
- Build and maintain a Physical AI sales playbook — qualification frameworks, battle cards, objection handling, and vertical\-specific ROI models — for the entire direct sales force to replicate
- Act as the voice of the field back to Product, Product Marketing, and Engineering — translating customer objections and buying patterns into sharper positioning and roadmap input
- Represent Vention at NVIDIA GTC, Automate, A\&D trade events, CPG industry forums, and customer site visits globally
### What You Bring to the Table
You’ll bring:
- 5–8 years in solutions engineering, technical pre\-sales, or field applications in industrial robotics, computer vision, or manufacturing automation — with evidence of progressive commercial ownership
- Hands\-on fluency with robotic systems — you can credibly discuss 6\-DoF pose estimation, grasp planning, collision avoidance and latest sensor\-to\-action policies with a robotics engineer and immediately translate that into business impact for a plant operations leader
- Working knowledge of AI/ML applied to industrial perception — familiarity with foundation models, simulation\-to\-reality transfer, and vision pipelines
- Demonstrated ability to navigate complex, multi\-stakeholder deals with long sales cycles — you are as comfortable building an ROI model as debugging an integration
- Domain exposure to at least one of the three priority verticals: industrial machinery, aerospace \& defense, or CPG
- Experience operating as an overlay or pre\-sales specialist supporting a direct sales force, plus the maturity to lead strategic accounts independently when required
- Comfort with 30–40% travel to customer sites, partner facilities, and industry events
### What We Offer
- Career pathing: Real opportunities to grow through personalized development plans, bi\-annual employee reviews, and mentorship program
- Professional development: Continuous training in performance management, inclusive leadership, leadership operating model, team building, and giving/receiving feedback
- Gender diversity \& inclusion: Pay equity reviews, inclusive policies, and a Women’s Employee Resource Group offering networking, mentorship, and quarterly learning sessions.
- Hybrid work: Enjoy flexibility with our hybrid model, allowing you to work from home on select days.
- Community engagement: Two paid volunteering days per year to give back to causes you care about.
- Team events: All year round employee events including annual kick\-off, employee summit, quarterly happy hours, and department events.
- Comprehensive benefits: A complete group benefits plan for you and your family that start day one.
- Top\-up and benefits for new parents: Additional financial support and resources to support employees during their transition to parenthood.
What to Expect in Your Interview
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- Initial Call
- Meet the Team
- Case or take\-home interview
- Decision \& Offer
We’re committed to making every step of the process inclusive and accessible. If you require accommodations at any stage, please let us know, we’ll ensure you have what you need to succeed.
### Vention’s culture
Vention is an uplifting environment for high achievers. Thinking that Vention’s culture would keep you energized? See our full culture guide here.
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Other Links You Might Find Useful:
Demo day video: https://www.youtube.com/watch?v\=jRSc\_brCQAw
Culture video: https://www.youtube.com/watch?v\=eszZu4c4ipE
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vention, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Vention AI Hiring
Vention has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Detroit, MI, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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