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Req ID: 136149
Remote Position: Yes
Region: Americas
Country: USA
Summary
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The PM will manage the rolling out of AI Initiatives across the HPS division. They are accountable for planning and directing design engineers and other technical engineers working on specific projects. They manage the development, implementation, and evaluation of complex designs. They are responsible for managing large projects with complex scope, multiple streams of work and inter\-dependencies.
Detailed Description
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Performs tasks such as, but not limited to, the following:
- Manage multiple high visibility programs (multiple projects per program) of high complexity (technology, organizationally) and/or business impact. Lead Cross\-functional, global core team(s), kick off programs, establish schedules and drive meetings.
- Present to, communicate with and influence segment/LOB business leaders.
- Lead/develop company wide, cross functional or complex process initiatives or business strategy.
- Demonstrate ability to grow cross functional team members by identifying development opportunities and coaching Program Core Team members.
- Recognised expert (go to person) in 3 or more skills associated with PM role. May have industry recognition as an expert. Typically leads Skill Group
- Present and communicate status to the business leaders and customers. Review and interpret customer specifications and provides customer feedback
- Coordinate site\-wide deployment efforts.
- Implement change as directed in the product lifecycle process and recommends process improvements
- Plan the overall program and monitor the progress. Drive the creation, review, approval and update of the Program Plan/WBS including resources.
- Daily program management throughout the program life
- Drive the program core team to meet or exceed program objectives (Cost, Quality, Schedule, Features, Fulfillment/Continuity of Supply, Solution delivery across products \& service offerings, Customer Specific Needs)
- Define program governance (controls)
- Manage the program’s budget. Forecast actuals against plan/quote for income/revenue, cost/labour \& expense
- Manage risks and issues and taking corrective measurements
- Coordinates the projects and their interdependencies. Manage and utilize resources across projects
- Align the deliverables (outputs) to the program’s “outcome” with the aid of the business change manager
- Manage the main program documentation, such as the Program Initiation document
Knowledge/Skills/Competencies
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Technical Understanding: While not necessarily needing to be a deep expert, program managers should have a good grasp of accelerated compute and AI technologies . This understanding helps in communicating effectively with technical teams and understanding project requirements.
Project Management:Strong project management skills are essential to coordinate various aspects of accelerated compute programs, including scope definition, resource allocation, scheduling, risk management, and stakeholder communication.
Domain Knowledge: Depending on the industry the accelerated compute program serves, having domain\-specific knowledge can be crucial. Understanding the business context and specific challenges within the industry helps in the execution of server and AI systems programs that truly address the needs of the stakeholders.
Communication Skills: Effective communication is key to aligning diverse stakeholders, including technical teams, business leaders, clients, and end\-users. Program managers need to translate technical jargon into understandable language for non\-technical stakeholders and vice versa. It is imperative that you are able to effectively and concisely communicate program updates, issues and schedule and financial impacts to the leadership team.
Strategic Thinking: Our programs often have long\-term implications and our Program managers play a critical role in thinking strategically, considering the broader organizational goals and how our programs fit into them. This involves planning for scalability, sustainability, and future advancements in accelerated compute technology.
Risk Management:Accelerated compute technologies are fast moving, dynamic with complex interdependencies that is prone to risk. Program managers need to work with various stakeholders to identify and mitigate these risks proactively to ensure project success.
Team Leadership:Leading multi\-disciplinary teams comprising engineers, domain experts, supply\-chain and technology parters requires strong leadership skills. Program managers should inspire and motivate team members, foster collaboration, and resolve conflicts effectively.
Adaptability:The field of AI is rapidly evolving, with new technologies and methodologies emerging regularly. Program managers need to stay updated with the latest trends and be adaptable to changes in project requirements or technological advancements.
Financial Acumen: Understanding budgeting, resource allocation, and financial implications of AI projects is crucial for program managers to ensure projects are delivered within budget and provide a positive return on investment.
Physical Demands
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- Duties of this position are performed in a normal office environment.
- Duties may require extended periods of sitting and sustained visual concentration on a computer monitor or on numbers and other detailed data.
- Repetitive manual movements (e.g., data entry, using a computer mouse, using a calculator, etc.) are frequently required.
- Occasional travel may be required.
Salary
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The stated range includes Base Salary and target Short\-Term Incentive (STI) compensation only. A comprehensive benefits package is offered in addition to this range.
The salary range described in this posting is an estimate by the Company, and may change based on several factors, including by not limited to a change in the duties covered by the job posting, or the credentials, experience or geographic jurisdiction of the successful candidate.
$141,000\-221,000
The range described in this posting is an estimate by the Company, and may change based on several factors, including but not limited to a change in the duties covered by the job posting, or the credentials, experience or geographic jurisdiction of the successful candidate.
Typical Experience
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- 11 to 14 years.
Typical Education
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Bachelor degree or consideration of an equivalent combination of education and experience.
Educational Requirements may vary by Geography
Notes
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This job description is not intended to be an exhaustive list of all duties and responsibilities of the position. Employees are held accountable for all duties of the job. Job duties and the % of time identified for any function are subject to change at any time.
Celestica 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, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category under applicable federal, state, and local laws.
This policy applies to hiring, promotion, discharge, pay, fringe benefits, job training, classification, referral and other aspects of employment and also states that retaliation against a person who files a charge of discrimination, participates in a discrimination proceeding, or otherwise opposes an unlawful employment practice will not be tolerated. All information will be kept confidential according to EEO guidelines.
Celestica is an E\-Verify employer.
Location: This is a remote position, with travel as necessary. We are open to considering candidates close to any of our US locations in Massachusetts, Pennsylvania, Minnesota, Texas, Arizona, Oregon or California as well as locations near major airports such as the Northeast, Southeast, Midwest and Pacific Coast.
COMPANY OVERVIEW:
Celestica (NYSE, TSX: CLS) enables the world’s best brands. Through our recognized customer\-centric approach, we partner with leading companies in Aerospace and Defense, Communications, Enterprise, HealthTech, Industrial, Capital Equipment and Energy to deliver solutions for their most complex challenges. As a leader in design, manufacturing, hardware platform and supply chain solutions, Celestica brings global expertise and insight at every stage of product development – from drawing board to full\-scale production and after\-market services for products from advanced medical devices, to highly engineered aviation systems, to next\-generation hardware platform solutions for the Cloud. Headquartered in Toronto, with talented teams spanning 40\+ locations in 13 countries across the Americas, Europe and Asia, we imagine, develop and deliver a better future with our customers.
Celestica would like to thank all applicants, however, only qualified applicants will be contacted.
Celestica does not accept unsolicited resumes from recruitment agencies or fee based recruitment services.
Salary Context
This $141K-$221K range is above 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 Celestica, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $141K to $221K.
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.
Celestica AI Hiring
Celestica has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $221K - $221K.
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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