Interested in this AI/ML Engineer role at Jacobs?
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Location Dallas, Texas, United States Capabilities Project Management Office Setup Hybrid Job ID #46182
Industry Advanced Manufacturing At Jacobs, we're challenging today to reinvent tomorrow by solving the world's most critical problems for thriving cities, resilient environments, mission-critical outcomes, operational advancement, scientific discovery and cutting-edge manufacturing, turning abstract ideas into realities that transform the world for good.
Your impact This job opportunity is available at ANY location in the United States. Ideally, selected candidates will be near a JACOBS U.S. based office, but we intend to hire the "best" candidates.
Jacobs is the #1-ranked design firm on ENR’s Top 500 Design Firms list — for the seventh consecutive year — and the #1 Program Management firm globally (ENR, 2026). We are not riding the data center wave; we are engineering it. Jacobs’ Data Center Digital Twin solution, built on NVIDIA’s Omniverse DSX Blueprint, was highlighted in Jensen Huang’s GTC keynote in March 2026, placing Jacobs at the technical frontier of gigawatt-scale AI factory design.
This is your opportunity to elevate your career to the next level — leading complex, multi-discipline programs as a Project Manager working alongside the most recognized Data Center clients in the industry. As a Project Manager, you will drive project execution strategy, oversee project performance, manage client relationships, and ensure seamless collaboration across technical disciplines. You will be accountable for project planning, financial performance, resource coordination, and proactive issue resolution while helping maintain the high standards of delivery excellence expected by Jacobs' clients. Success requires strong leadership, exceptional communication skills, sound business judgment, and the ability to guide teams through fast-paced, mission-critical projects where schedule certainty and operational reliability are paramount.
At Jacobs, you will do what you are most passionate about, at a scale and with a client roster that defines the future of AI infrastructure.
Key Responsibilities
Project Execution & Controls
- Own the project management plan: scope, schedule, budget, fee forecasting, contract administration, and risk register.
- Manage Jacobs engineering fee budgets and subcontract scopes; track earned value, cost-at-completion, and variance monthly
- Lead project schedule development in P6 or MS Project; drive critical path analysis and recovery planning across multi-discipline milestones (Typical: BOD, 30%/60%/IFC design, permit, construction)
- Administer prime contracts and subconsultant agreements; manage change order processes with disciplined scope and cost substantiation
- Delivery & Technical Coordination
- Coordinate multi-discipline engineering teams (Architectural, site/Civl, Structural, MEP, process) from concept through construction administration; chair weekly design coordination and BIM clash resolution meetings
- As Owner's Architect/Engineer: represent the client's interests through design review, RFI response, submittal processing, and construction observation; manage document issuance and schedule adherence.
- Interface with owner/operator technical teams on design criteria, standards compliance, and program-level reporting
- Quality, Safety & Commissioning
- Enforce Jacobs quality management plan; conduct or facilitate key issuance IFC peer reviews and design QA/QC reviews (typically 30%/60%/IFP/IFC milestones)
- Champion Jacobs BeyondZero® safety culture; review contractor safety plans and conduct site safety observations during construction administration phases
Client Relationships & Growth
- Serve as primary client point of contact; manage project-level communication, monthly program dashboards, and issue escalation
- Support business development pursuits: contribute to technical proposals, BOE development, staffing plans, and client presentations for Data Center and Advanced Manufacturing opportunities.
Here's what you'll need Required Qualifications - Here's What You'll Need
- Bachelor’s degree in engineering, Architecture, or related discipline (or equivalent experience)
- 5-12 years of progressive project management experience on industrial or technically complex facility programs
- Minimum 2 years demonstrated PM experience on Data Center, Advanced Technology, or Semiconductor projects as PM— not solely as project engineer/Architect or support PM
- Proficiency in project controls tools: P6 or MS Project (scheduling), cost tracking systems, and earned value reporting
Preferred Qualifications - Ideally, You'll Also Have
- Registered Architect or PE (Professional Engineer) Electrical, Mechanical, Civil, or Structural; multi-state reciprocity valued
- PMP (Project Management Professional) PMI certification required or in progress
- Experience on hyperscale cloud, AI Data Centers, or advanced semiconductor fab programs
- LEED AP BD+C, CEA, or DBIA credential — differentiates on sustainability-driven and Design-Build pursuits
- Familiarity with hyperscale design standards: ASHRAE 90.4, Uptime Institute Tier classification, BICSI TDMM, NEC 708
Why Jacobs
Posted Salary Range: Minimum 134,500.00 Posted Salary Range: Upper 185,000.00 Our health and welfare benefits are designed to invest in you, and in the things you care about. Your health. Your well-being. Your security. Your future. Employees have access to medical, dental, vision, and basic life insurance, a 401(k) plan, and the ability to purchase company stock at a discount. Eligible employees may also enroll in a deferred compensation plan or the Executive Deferral Plan. Jacobs has an unlimited U.S. Personalized Paid Time Off (PPTO) policy for full-time salaried/exempt employees, seven paid holidays, and caregiver leave. And certain roles may be eligible for additional rewards, including merit increases, performance discretionary bonus, and stock.
The base salary range for this position is $134,500.00 to $185,000.00. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. Job posted on October 01, 2026. This position will be open for at least 3 days.
We value collaboration and believe that in-person interactions are crucial for both our culture and client delivery. We empower employees with our hybrid working policy, allowing them to split their work week between Jacobs offices/projects and remote locations enabling them to deliver their best work.
Your application experience is important to us, and we’re keen to adapt to make every interaction even better. If you require further support or reasonable adjustments with regards to the recruitment process (for example, you require the application form in a different format), please contact the team via Careers Support.
Salary Context
This $134K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Jacobs, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($159K) sits 25% below the category median. Disclosed range: $134K to $185K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Jacobs AI Hiring
Jacobs has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dallas, TX, US, St Paul, MN. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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