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About This Role
About 3E:
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, or Canton, OH, or Charlottesville, VA.
What You’ll Do
- Translate Product intent into clear, testable, and executable work, using readiness checks and automation to ensure requirements are ready before development begins.
- Strengthen traceability across requirements, user stories, testing, releases, and deployments.
- Own program-level delivery outcomes, including lead time, quality, predictability, blocker resolution, and release readiness.
- Identify cross-program dependencies, blockers, and delivery risks early and drive them to resolution.
- Build and use delivery metrics, including cycle time, lead time, work in progress, throughput, quality, and predictability, to guide decisions about scope, timelines, and trade-offs.
- Use forecasting, data, and AI-generated insights to anticipate risks and support proactive delivery decisions.
- Apply AI, automation, and delivery tooling to reduce manual coordination and improve the speed and quality of decision-making.
- Partner with Product, Engineering, QA, DevOps, and other stakeholders to strengthen delivery alignment and embed governance into release-readiness processes.
- Create reusable delivery practices, automations, and frameworks that can scale across teams and programs.
- Lead teams through changes in tools, processes, expectations, and ways of working, including greater adoption of flow-based delivery where appropriate.
- Communicate delivery progress, risks, and recommendations clearly to senior stakeholders, with a focus on decisions and outcomes.
What Makes You a Great Fit
Minimum Qualifications:
- Demonstrated experience leading complex software delivery initiatives across multiple teams, programs, and dependencies, with accountability for results rather than coordination alone.
- Experience applying Scrum, Kanban, SAFe, or hybrid approaches and using flow metrics to identify risks and drive decisions about scope, timelines, and trade-offs.
- Technical fluency across the software development lifecycle, including requirements, testing, releases, deployments, CI/CD concepts, and production support.
- Hands-on experience using AI tools, automation, or data-driven workflows to improve delivery, engineering, or operational processes.
- Ability to evaluate AI-generated output, determine when additional validation is needed, and apply sound judgment before acting on it.
- Experience leading change, establishing process discipline, and improving accountability in a complex or evolving environment.
- Excellent communication and relationship-building skills, including the ability to translate delivery and technical complexity into clear recommendations and influence across Product, Engineering, QA, DevOps, and Operations.
- Located in the U.S. Eastern Time Zone, able to work effectively across North American, European, and Asian time zones
- Physically located in the Eastern Time Zone (US), with the ability to work without sponsorship
Preferred Qualifications:
- Experience using Python, JavaScript, or a similar language to build scripts, automations, internal tools, or AI-enabled workflows.
- Experience designing, building, or operating CI/CD pipelines, cloud environments, containers, or infrastructure-as-code.
- DevOps, reliability, or MLOps experience, including model deployment, monitoring, versioning, or governance.
- Experience supporting a transition to Kanban or another flow-based delivery model.
- Experience building internal tools, AI agents, automations, or workflow solutions adopted by software delivery teams.
- Familiarity with AI governance concepts or frameworks, such as the NIST AI Risk Management Framework or EU AI Act.
- Relevant cloud, AI, project delivery, or Agile certifications.
- Experience leading organizational transformation 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 any legally protected categories. For applicants who would like to request accommodation, please contact us here
Visit our Website or follow us on LinkedIn
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: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($97K) sits 54% below the category median. Disclosed range: $90K to $105K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
3E AI Hiring
3E has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jacksonville, FL, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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