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About This Role
We are seeking a highly motivated Machine Learning Engineer to join our core research and development team, focused on video understanding and segmentation. In this role, you will build the systems that let us search, decompose, and describe massive volumes of egocentric and human\-robot video at scale — turning raw, unstructured footage into structured, searchable, and richly annotated training data. You will work across video/image embedding models, LLM\-based video understanding, and agentic pipelines that orchestrate multiple models into end\-to\-end workflows. This is a foundational role that directly shapes the data quality and scalability of our entire training data platform.
Responsibilities
- Build and optimize video/image embedding pipelines using CLIP\-style and other vision\-language embedding models to power large\-scale, multi\-modal video search and retrieval.
- Develop LLM\-based video understanding systems for semantic indexing, summarization, and question\-answering over long\-form egocentric and third\-person video.
- Design and implement instruction\-level and action\-level video chunking/segmentation algorithms that decompose long videos into structured, temporally\-aligned clips.
- Build automated video captioning systems that combine vision\-language models and LLMs to produce fine\-grained, temporally\-grounded descriptions of actions and scenes.
- Architect agentic systems and orchestration pipelines that chain embedding, captioning, retrieval, and LLM reasoning steps into reliable, end\-to\-end video understanding workflows.
- Develop and scale video search infrastructure (vector indexing, retrieval, ranking) to support semantic and multi\-modal queries over millions of video clips.
- Collaborate with annotation, data engineering, and robotics teams to integrate video understanding outputs into downstream training pipelines for embodied AI and robot learning.
- Evaluate and benchmark embedding models, LLMs, and agentic frameworks against production needs; track frontier research and bring relevant techniques into the platform.
- Contribute to internal tooling, documentation, patents, and open\-source initiatives where applicable.
- Mentor junior engineers and interns, and help shape the long\-term technical roadmap for video understanding.
Minimum Qualifications
- MS or PhD in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience.
- 3\+ years of hands\-on experience in computer vision or multi\-modal machine learning, with direct experience in video understanding tasks.
- Strong proficiency in Python and PyTorch, with solid software engineering fundamentals.
- Hands\-on experience with CLIP or similar vision\-language/video embedding models for retrieval or representation learning.
- Experience building or fine\-tuning LLM\-based systems for video/image understanding (e.g., captioning, video QA, summarization).
- Familiarity with agentic system design — tool use, multi\-step reasoning, and orchestration frameworks (e.g., LangChain, LlamaIndex, or custom agent loops).
- Experience working with large\-scale video data pipelines and vector search/retrieval infrastructure (e.g., FAISS, Milvus, or equivalent).
Preferred Qualifications
- PhD with a research focus in video understanding, multi\-modal learning, or vision\-language models.
- Experience with temporal action segmentation, action localization, or instruction\-level video chunking algorithms.
- Experience working with egocentric video datasets or head\-mounted\-device (HMD) captured data.
- Track record of deploying production\-scale video search or retrieval systems.
- Experience integrating foundation or vision\-language models (e.g., CLIP, VideoCLIP, RT\-1/VLA variants) into perception or decision\-making pipelines.
- Publications in top\-tier computer vision or ML venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, etc).
- Experience with humanoid robotics or embodied AI data pipelines is a plus.
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 MaxInsights, 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 $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.
MaxInsights AI Hiring
MaxInsights has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, 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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