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About This Role
Location: Santa Clara, CA
About the Company
We are building the data infrastructure powering the next generation of Embodied AI and robotics foundation models.
Our platform enables robotics companies and AI labs to access large\-scale, high\-quality multimodal training data through advanced data collection, simulation, annotation, and delivery systems.
The Role
We are looking for a Technical Program Manager who can own complex AI data projects from customer requirements through final delivery.
You will serve as the central coordinator between customers, engineering teams, data operations, and global teams to ensure successful execution of robotics data projects.
This role is ideal for someone who enjoys solving ambiguous problems, managing cross\-functional teams, and building scalable processes in a fast\-growing AI startup.
Responsibilities
Program Ownership
Own end\-to\-end delivery of robotics AI data projects.
Manage project scope, timeline, quality, and customer expectations.
Build project plans, milestones, and execution frameworks.
Technical Coordination
Translate customer AI model requirements into actionable data specifications.
Work with engineering, algorithm, and data teams to define solutions.
Understand data pipelines, sensor data, annotation workflows, and ML training requirements.
Quality \& Operations Management
Establish project tracking systems and quality control processes.
Monitor production progress and identify execution risks.
Drive continuous improvement of data workflows.
Cross\-functional Collaboration
Coordinate teams across US, China, and Southeast Asia.
Communicate project status and resolve operational challenges.
Build repeatable processes for scaling AI data delivery.
Qualifications
2\-5 years of experience in Technical Program Management, Program Management, Operations, or Engineering Project Management.
Experience working with engineering teams and technical products.
Strong organizational and problem\-solving skills.
Excellent communication skills in English.
Ability to manage multiple stakeholders in a fast\-moving environment.
Preferred
Experience in AI, robotics, autonomous vehicles, cloud infrastructure, or data platforms.
Experience managing large\-scale data operations.
Startup experience.
What We Offer
Competitive compensation package.
Opportunity to work directly with leading AI and robotics companies.
High ownership role with significant growth opportunities.
Build foundational infrastructure for the future of robotics.
JD 3:Forward Deployed Engineer – Robotics AI Data Solutions
Location: Santa Clara, CA
About the Company
We are developing next\-generation AI infrastructure for robotics companies by providing high\-quality multimodal data solutions for embodied intelligence.
Our mission is to accelerate the development of intelligent robots by solving one of the biggest challenges in AI today: scalable, high\-quality training data.
The Role
We are looking for a Forward Deployed Engineer (FDE) who will act as the technical bridge between customers and our engineering teams.
You will work directly with robotics and AI teams to understand their data requirements, design solutions, generate customized datasets, and ensure successful deployment into customer workflows.
This role combines software engineering, AI data, robotics understanding, and customer collaboration.
Responsibilities
Customer Technical Engagement
Work directly with robotics and AI research teams to understand data requirements.
Translate customer needs into detailed technical specifications.
Support solution design and implementation.
Data Engineering \& Production
Build scripts and tools for multimodal data processing.
Develop workflows for dataset generation, validation, and quality control.
Support large\-scale data production pipelines.
Robotics AI Collaboration
Work with engineering and research teams on embodied AI data challenges.
Analyze dataset quality and provide feedback for model improvement.
Support continuous iteration between data generation and model performance.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, Robotics, or related fields.
Strong Python programming skills.
Experience working with data pipelines, ML workflows, or software engineering.
Strong analytical and problem\-solving ability.
Comfortable working directly with customers and technical teams.
Preferred
Experience with robotics, autonomous driving, computer vision, or AI infrastructure.
Knowledge of: 3D geometry
Simulation tools (Isaac Sim, MuJoCo, PyBullet)
Multimodal data processing
ML training workflows
Experience in customer\-facing technical roles.
What We Offer
Work on cutting\-edge Embodied AI technology.
Direct collaboration with leading robotics companies and AI researchers.
High ownership role in a rapidly growing startup.
Competitive compensation and equity opportunities.
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 Glint Tech Solutions, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Glint Tech Solutions AI Hiring
Glint Tech Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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