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About This Role
About 3E:
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3E is a mission\-driven company dedicated to creating a safer and more sustainable world. We offer award\-winning regulatory intelligence and AI solutions to help organizations protect people and products. With over 35 years of experience and a global presence, we empower our customers with innovative compliance solutions.
You will get to work at a company that combines startup agility with the stability of an industry leader. Partner with enterprise customers and global experts to advance environmental safety and solve complex data science challenges.
*Join us and make an impact!*
About the Role:
The Technical Project Manager (AI) is an engineering\-embedded leadership role that transforms how 3E ships software by combining technical execution, program ownership, and change leadership to increase speed, quality, and predictability across programs.
Reporting to the AI Delivery Team Lead, you will bridge product intent and engineering execution. In this role, you will build AI\-enabled delivery systems that surface risk early, automate routine coordination, and embed governance directly into pipelines so teams can deliver faster with greater confidence.
If you are someone who has shipped production systems in a technical capacity, wants to build solutions instead of running status meetings, and wants to scale your impact by improving how multiple teams deliver, this role is built for you.
Location: This position supports remote work and should be based near one of our U.S. East Coast office locations: Bethesda, MD, Charlottesville, VA, or Canton, OH.
What You’ll Do
- Translate product intent into executable tasks and establish automated validation to ensure that requirements are clear, testable, and ready before development begins.
- Implement traceability across requirements, user stories, tests, and deployments.
- Build automated validation, ensuring requirements are clear, testable, and ready before development begins
- Create cross\-program detection for dependencies, blockers, and delivery risks, providing real\-time visibility through automation.
- Drive resolutions by actively engaging engineers, developing tools, and reducing the need for repeated interventions.
- Build and maintain flow metrics infrastructure (including cycle time, lead time, work in progress, throughput, and predictability) to inform decisions regarding scope, timelines, and trade\-offs.
- Deploy forecasting and risk models to facilitate proactive delivery decisions.
- Eliminate routine coordination through automation, allowing more time for judgment and action.
- Write production\-quality code (in Python, JavaScript, or equivalent languages) to address delivery challenges, review AI\-generated code, and debug complex systems.
- Design and orchestrate multi\-agent AI workflows throughout the delivery lifecycle.
- Integrate DevOps and MLOps practices, embedding governance into CI/CD processes and ensuring release readiness.
- Lead transformation by actively participating in the process, assisting teams in adopting new tools, expectations, and ways of working.
What Makes You a Great Fit
Minimum Qualifications:
- Delivery leadership experience across multiple global teams and dependencies, with a track record of owning delivery results (not just coordinating)
- Strong understanding of Agile and flow\-based delivery (Scrum, Kanban, or hybrid) and proven use of flow metrics to drive decisions
- Strong coding proficiency in Python, JavaScript, or similar, including the ability to write production\-quality code, review AI\-generated code, and debug
- Proven experience designing, building, and operating CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or similar)
- Practical cloud experience in AWS, Azure, or GCP, including containers and infrastructure\-as\-code or equivalent deployment automation
- Hands\-on experience using AI coding assistants (Claude, Copilot, Cursor, ChatGPT, or similar) in software engineering workflows
- Excellent communication and relationship\-building skills, forming recommendations using data and AI insight, and with the ability to influence across levels and functions
- Physically located in the Eastern Time Zone (US), with the ability to work without sponsorship
- Willingness and ability to work effectively across multiple time zones (North America, Europe, and Asia).
Preferred Qualifications:
- DevOps reliability or MLOps experience (model deployment, monitoring, versioning, governance)
- Experience building internal tools or automations adopted by software engineering teams
- Familiarity with AI governance concepts or frameworks (for example, NIST AI RMF or EU AI Act concepts)
- Relevant certifications (cloud, AI, or delivery/agile, such as AWS, Azure, GCP, PMP, CSM/PSM)
- Experience leading organizational transformation initiatives in a global, distributed environment
- Proficiency in German or French
What’s In It for You
- Solve Complex, High\-Impact Problems: Use AI to address mission\-critical challenges that directly influence revenue and drive business value.
- Work with Proprietary Data: Leverage exclusive datasets and real\-world signals to build intelligent systems with lasting competitive advantage.
- Learn from Proven Leadership: Partner with seasoned executives who’ve scaled AI and product teams at high\-growth companies.
- Best of Both Worlds: Experience the pace and innovation of a startup culture with the stability and resources of a trusted global enterprise.
Our US Benefits Include:
- Health, dental, and vision insurance
- Life insurance and disability coverage
- Open PTO and parental leave
- 401(k) plan with company matching
- Employee assistance program
- Voluntary supplemental benefits (Accident, Hospital Indemnity, Critical Illness)
3E is currently authorized to hire in the following U.S. states:
Alabama, Arizona, California (excluding Los Angeles), Colorado (excluding Denver), Connecticut, Delaware, District of Columbia, Florida, Georgia, Illinois (excluding Chicago), Indiana, Kansas, Kentucky, Maryland, Massachusetts, Michigan, Minnesota, Nevada, New Jersey, New York (excluding New York City), North Carolina, Ohio, Oklahoma, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Virginia, and Washington.
Disclosures:
3E is committed to a diverse and inclusive work environment. 3E is an equal opportunity employer and does not discriminate based on race, nationality, gender, gender identity, sexual orientation, protected veteran status, age, disability, or any other legally protected status. For applicants who would like to request accommodation, please send an email to [email protected]
Visit us at https://www.3eco.com/
Follow us at https://www.linkedin.com/company/3e\-safer\-world/
Privacy Policy and Candidate Privacy Notice
Agencies: 3E is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at 3E via email, the Internet, or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of 3E. No fee will be paid in the event the candidate is hired by 3E because of the referral or through other means.
Compensation Range: $90K \- $105K
Salary Context
This $90K-$105K range is in the lower quartile 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 3E, 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 ($97K) sits 46% below the category median. Disclosed range: $90K to $105K.
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.
3E AI Hiring
3E has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bethesda, MD, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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