Interested in this AI/ML Engineer role at DIVERGENT?
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About This Role
Divergent is a technology company that has architected, invented, built, and commercialized an end\-to\-end factory system called the Divergent Adaptive Production System (DAPS) that comprehensively uses machine learning to optimally engineer, additively manufacture, and flexibly assemble complex integrated vehicle structures and subsystems. Products created using DAPS are superior in performance, lower in cost, rapidly customizable to meet mission and customer\-specific requirements, faster to market, and scalable on demand to high volume production. Divergent is a qualified Tier 1 supplier to global automotive OEMs, and Divergent is now expanding to support mission critical needs in the Aerospace and Defense sector. Join us to be a part of this transformative journey, where your impact will shape the future of technology and production.
Purpose
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We are looking for an AI Solutions Engineer who not only builds cutting‑edge generative‑AI systems but also serves as the AI Enablement lead for the organization. You'll design, develop, and productionize large‑language and multimodal model solutions while dedicating a significant portion of your time to help teams adopt AI responsibly and effectively.
The Role
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Help Build \& Deploy AI Solutions
- Collaborate with cross\-functional teams to integrate and optimize generative AI solutions into products and processes.
- Develop domain specific AI software using RAG and Agentic AI.
- Collaborate with the data engineers, product teams, people operations, and legal to ensure compliant and responsible AI.
- Implement usage monitoring and bias mitigation to improve result quality.
AI Adoption \& Enablement
- Act as the primary internal AI champion, providing guidance, training, and best‑practice workshops for the company.
- Create and maintain self‑service resources (playbooks, tutorials, templates) that empower users to build and evaluate AI solutions safely.
- Host regular "AI office hours," one‑on‑one coaching sessions, and webinars to raise AI expertise across the company.
- Partner with compliance and ethics teams to embed responsible‑AI checks into everyday workflows.
- Gather user feedback, translate it into product requirements, and drive iterative improvements to AI tools and platforms.
Basic Qualifications
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- B.S. in Computer Science/AI/ML (or related) with 3\+ years AI/ML engineering experience.
- Proficient in programming languages such as Python.
- Understanding of LLMs and multimodal models with their performance considerations and use cases.
- Knowledge about prompt engineering, RAG, and building AI Agents.
- Experience with ML/LLM libraries such as vLLM, LangChain, PyTorch, and HuggingFace.
- Practical experience developing, deploying, and scaling AI Agents in a production environment.
- Ability to translate complex technical concepts for non‑technical audiences and collaborate cross‑functionally.
- Experience running trainings to teach and promote AI best practices.
- Ability to create self‑service enablement assets (playbooks, tutorials, reusable component libraries).
- Proven track record of gathering user feedback and iterating on AI tools to improve adoption.
- Ability to lawfully access information and technology that is subject to US export controls
Preferred Qualifications
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- M.S. in Computer Science, AI, ML, or related field.
- Proficiency in TypeScript.
- Knowledge of ethical AI, compliance frameworks, and safety standards.
- Experience with multimodal pipelines and traditional AI/ML methods.
- Hands‑on MLOps: CI/CD, Docker, Kubernetes.
Work Environment
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- Hybrid
What We Offer:
- Holistic Compensation Package: Enjoy a world\-class compensation package that includes a competitive salary, equity plan, and discretionary results\-based incentive bonus opportunities, ensuring you're truly valued for your contributions.
- Wellness and Time Off: Embrace a healthy lifestyle with paid vacation, sick time, and company holidays, including a year\-end shutdown to recharge. We support growing families with paid parental leave, recognizing the importance of bonding time.
- Comprehensive Health and Wellness: Prioritize your well\-being with our comprehensive health and wellness benefits, offering both HMO and Premium PPO options. Additionally, benefit from company\-sponsored life insurance and short and long\-term disability coverage for peace of mind.
- Investment in Growth: We're committed to your professional development. Take advantage of reimbursement opportunities for learning and development initiatives, empowering you to continuously expand your skill set and reach peak performance.
- Collaborative and High\-Performing Environment: Join our collaborative, dynamic, and high\-performing team within a fast\-paced, mission\-driven company. Together, we're disrupting the traditional manufacturing industry, fostering innovation, and integrating people and technology to reduce our footprint.
*Equal Employment Opportunity*
*Divergent is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected characteristic. Divergent provides affirmative action in employment for qualified Individuals with Disabilities and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act.*
*EEO Poster*
*In order to adjust to changes in our business, it may become necessary to add, remove or modify certain duties and responsibilities, or to reassign you to another job position. From time to time you may be asked to work on special projects or to assist with other work. Your cooperation and assistance in performing such additional work is expected.*
*E\-Verify: Right to Work*
*Our company participates in* *E\-Verify**. E\-Verify is a program that electronically confirms a candidate's eligibility to work in the United States after completing the Employment Eligibility Verification (Form I\-9\). The information provided on the Form I\-9 is compared to the records contained in the Social Security Administration and Department of Homeland Security (DHS) databases. This helps employers verify the identity and employment eligibility of newly hired employees.*
*Eligibility to Work Poster (English)* *\|* *Eligibility to Work Poster (Spanish)*
*Los Angeles Fair Chance Initiative for Hiring Ordinance (FCIHO)*
*Pursuant to the Los Angeles Fair Chance Initiative for Hiring Ordinance (FCIHO), we will consider for employment qualified applicants with arrest and conviction records.*
*No agencies, no solicitations, and no calls please.*
Salary Context
This $122K-$168K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At DIVERGENT, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($145K) sits 20% below the category median. Disclosed range: $122K to $168K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
DIVERGENT AI Hiring
DIVERGENT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $168K - $168K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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