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About This Role
About 4MP
At 4MP, we are building a new technological foundation for precision manufacturing. Our systems enable CNC machines to self\-measure, self\-correct, and continuously improve their performance – transforming conventional machine tools into *autonomous* precision systems.
Backed by Xora Innovation (“Xora”) and extensively vetted by recognized experts across industry and government laboratories, 4MP is at the beginning of a category\-defining journey. We are transitioning from early development into building a visionary company that will redefine how precision manufacturing is done globally.
About Xora Innovation
Xora is a leading global AI and deep tech venture firm backed by Temasek, Singapore’s sovereign wealth fund. At Xora, we provide capital and long\-term commitment to exceptional entrepreneurs who are transforming essential industries.
Our investments focus on three key sectors: AI Infrastructure, Applied AI and Deep Tech. We are active as an early\-stage venture investor, preferring to enter at the Seed or Series A stage, and remain deeply committed as long\-term partners. We also collaborate with founders to form and launch high\-velocity startups based on strong market theses.
Xora’s portfolio companies include:
Celestial AI (Photonic Fabric™ technology platform, exited to Marvell Technologies)
Amperesand (next\-gen power infrastructure for AI data centers)
Bedrock Robotics (autonomous machines for construction)
Upscale AI (back\-end networking for data centers)
Vinci (physics\-driven AI for hardware simulation and design)
About the Role
We are seeking a hands\-on, visionary VP of AI who can architect, code, build, and lead from the front. You will work directly with the founding leadership team across software, machine physics, geometry, metrology, and data infrastructure to build the intelligence layer for a new category of manufacturing.
Joining at this seed stage means you will have a foundational voice in shaping the company, the technical architecture, and our future product direction. You will not simply lead an AI function; you will define how AI becomes the foundational layer of autonomous manufacturing.
What You Will Do
- Build and Manage the AI Organization: You will build and manage the AI organization at 4MP, while doing hands\-on technical work. You will recruit and mentor a world\-class team while architecting the AI modules, writing code, reviewing architecture, and building prototypes alongside them.
- Lead AI Architecture: Design and develop the intelligence layers that enable machines to interpret manufacturing intent, understand system deviations, and autonomously generate corrective actions.
- Integrate Multidisciplinary Data: Define how AI interacts with physics\-based models, machine telemetry, sensor data, CNC programs, and manufacturing context.
- Interpret \& Modify CNC Programs: Build AI systems to extract machining intent from G\-code, accounting for operator variability, CAM differences, and varied programming styles.
- Design Learning Frameworks: Develop feedback loops that combine AI reasoning with physics\-based models and sensor data, allowing machines to improve from each correction cycle and production result.
- Build Data Infrastructure: Create a scalable data architecture that captures manufacturing intelligence across machines, parts, tools, materials, and production environments.
- Create Simulation Environments: Define simulation and validation environments for training, testing, and scaling autonomous correction models.
- Drive Strategic Cross\-Functional Leadership: In close interaction with other functional leaders, you will contribute to defining the company strategy, technology and business roadmaps, and comprehensive project plans.
What We Require
- Hands\-On AI Management: Proven experience building and managing an AI organization while doing hands\-on technical work. You must be comfortable leading teams while actively contributing to the codebase.
- 10\+ Years of Experience: Deep background in AI, software engineering, machine learning architecture, robotics, intelligent systems, or complex technical platforms.
- Industrial Domain Expertise: Direct experience with robotics, autonomous systems, industrial systems, physics\-informed machine learning, reinforcement learning, control systems, simulation, digital twins, or real\-world telemetry.
- System Architecture Expertise: Deep experience designing and building production\-grade AI, ML, data, or software infrastructure from the ground up.
- Strong Coding and Data Proficiency: Exceptional hands\-on coding skills and ability to manage data pipelines, model orchestration, validation, feedback loops, and complex, messy, real\-world data.
- Core AI/ML Expertise: Strong, up\-to\-date understanding of optimization, statistical modeling, and system\-level AI design.
- Educational Foundation: Bachelor’s degree in Computer Science, Robotics, Engineering, Physics, Mathematics, or a related field.
What Will Set You Apart
- Advanced Degree: Master’s or PhD in Computer Science, Robotics, Engineering, Physics, Mathematics, or a related field.
- Startup \& Scaling Experience: An ideal candidate will bring a unique blend of agile startup experience alongside a proven track record of navigating the growth, product\-focused phase at a major AI organization (such as OpenAI, Anthropic, Google, Meta, etc.).
- “Can Do”, Driven Attitude: A track record of turning ambiguous, unsolved technical problems into reliable, working systems where no playbook existed.
- Systems Thinker: A focus on building foundational, long\-term scalable platforms and intelligent systems rather than just standalone models.
- Visionary Drive: You are not just looking for a senior title; you are looking for the opportunity to achieve a career\-defining milestone by building a platform that fundamentally reshapes an entire industry.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Xora Innovation, 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 Required
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 $178,940 based on 11,900 positions with disclosed compensation.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Xora Innovation AI Hiring
Xora Innovation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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