Interested in this AI/ML Engineer role at Mitalmor?
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The problem
The digital economy is the most instrumented environment in human history. Every click tracked, every dollar traced, in real time, globally. The physical economy, roughly $100 trillion a year, runs on clipboards, radios, and periodic manual counts. Hospitals cannot find equipment. Warehouses lose inventory they cannot see. Autonomous systems need continuous spatial ground truth and that layer does not exist at scale.
The signal was never missing. Every phone, wearable, and machine has been broadcasting the whole time. What was missing was a receiver cheap enough to deploy everywhere.
What we built
The Mitalmor Group's platform replaces a standard enterprise WiFi access point with a single ceiling\-mount node that passively identifies, locates, and authenticates every wireless device in range, sub\-10cm accuracy in three dimensions, across WiFi, BLE, UWB, cellular, and RFID at once. No tags, no cameras, no app, no opt\-in required.
Twenty issued patents. A working prototype validated at sub\-10cm. It works on the patient who will not wear a badge, the pallet nobody tagged, and the device that should not be on the floor.
Why this is a category
Every major wireless transition produced exactly one platform company. WiFi produced Cisco. Mobile produced Qualcomm. Nothing has ever played that role for the physical intelligence layer, the layer that tells every other system where things actually are. That is the company we are building, and the CEO who builds it with us gets in at the true ground floor.
Where we actually are
A very small founding team, prototype stage demonstrating the technology breakthrough, pre\-revenue, pre\-seed\-close. No sales team, no customer base, no commercial playbook. A validated technical breakthrough, a filed patent moat, we are not commercial yet, we are not a commercial product yet.
What you will own
The beachhead call across six markets with real funded pain (retail loss prevention, healthcare asset visibility, commercial real estate, corrections, financial services compliance, supply chain custody). The first enterprise customers, sourced from your own relationships. The seed raise, run by you with the co\-founders beside you. The commercial engine, built from nothing. The executive team through Series A. The board, the P\&L, and the narrative in every room from a retail CFO to a defense program office.
The co\-founders remain actively engaged on capital, relationships, and positioning. You will not be alone. But you will be in front.
Who tends to thrive here
Operators from supply chain visibility and asset tracking, industrial IoT and operational intelligence, RFID and item\-level inventory intelligence, warehouse and logistics automation, loss prevention and asset security technology, healthcare RTLS and clinical asset management, commercial real estate technology, or defense\-adjacent and dual\-use hardware\-software platforms are strongly encouraged to apply.
Strong candidates have sold into VP and C\-level buyers across operations, supply chain, IT, and asset management, often inside the same deal, and have built a commercial motion from zero.
Qualifications
- Demonstrated track record as CEO, President, founder, or equivalent senior leader at a deep tech, hardware\-software, IoT, or supply chain intelligence company, with verifiable commercial or exit outcomes
- Demonstrated success raising institutional venture capital, with partner\-level relationships at institutional funds, family offices, or strategic capital partners
- Demonstrated ability to land first enterprise customers in a market with no existing commercial playbook
- Technical fluency to hold a credible conversation on RF sensing, wireless infrastructure, edge compute, and spatial data platforms
- Comfort operating in defense\-adjacent and dual\-use environments, including with government customers
- Demonstrated success recruiting and retaining senior commercial and technical talent
- Bachelor's degree or equivalent professional experience
Compensation
Base salary of $150,000 to $250,000 annually following the seed close, commensurate with stage and experience, with a material step\-up at Series A. Meaningful founding equity commensurate with a CEO role at this stage. Performance bonus eligibility post\-raise. All details are discussed over the course of conversations, as raise size and other factors are taken into account.
Pay: $150,000\.00 \- $250,000\.00 per year
Application Question(s):
- Have you personally raised institutional venture capital as a CEO or founder? If yes, what was the total raised across all rounds?
- Have you taken a hardware, IoT, supply chain, or deep tech product from zero to a first signed enterprise customer? Name the company and what you closed.
- Name one institutional investor you have a direct, partner\-level relationship with who you would call today to lead a seed round.
Work Location: Remote
Salary Context
This $150K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Mitalmor, 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 $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $150K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Mitalmor AI Hiring
Mitalmor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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