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About This Role
D\-Wave Quantum Inc. (NASDAQ: QBTS) is a leader in the development and delivery of quantum computing systems, software, and services. It is the world’s first commercial supplier of quantum computers, and the first and only to offer dual\-platform quantum computing products and services, spanning both annealing and gate\-model quantum computing technologies. D\-Wave’s mission is to help customers realize the value of quantum today through enterprise\-grade systems available on\-premises and via its Leap™ quantum cloud service, which offers 99\.9% availability and uptime. More than 100 organizations across commercial, government, and research sectors trust D\-Wave to address complex computational challenges using quantum computing. Learn more about realizing the value of quantum computing today and how D\-Wave is shaping the quantum\-driven industrial and societal advancements of tomorrow: www.dwavequantum.com.
About the role
D\-Wave is seeking an experienced AI Platform DevOps Engineer to join our Product Development team. In this role, you will design, build, deploy, and operate AI\-powered tools, platforms, and workflows that improve developer productivity, automate repetitive tasks, reduce operational overhead, and accelerate the delivery of our quantum computing technologies.
Working alongside talented DevOps engineers and Product Development teams, you will help build and evolve our internal AI platform, integrating technologies such as Amazon Bedrock, AI agents, n8n, Artifactory, ArgoCD, and other supporting services into scalable, secure, and reliable development workflows.
You will play a key role in designing and operating AI\-powered development, on\-call, and agentic workflows, partnering across engineering teams to prioritize, implement, and continuously improve AI initiatives that enable innovation across the organization.
What you'll do
- Design, implement, and operate our internal AI tools platform
- Work with our development teams to streamline our internal AI build processes and release management (build processes and release management processes that incorporate AI (to function) and build processes and release management of AI\-related tools, solutions, workflows) via continuous integration and deployment pipelines
- Build and operate deployment pipelines for models, prompts, and evaluations, including versioning, cost tracking, and rollback strategies
- Participate in security reviews and compliance efforts, designing and implementing the security controls, access rules, and service configurations needed to meet those requirements
- Apply DevOps best practices to testing and monitoring, continuously improving the performance, durability, and reliability of our internal AI platform
- Respond to operational incidents and development questions related to our internal AI platform and perform root\-cause analysis
- Continuously monitor and improve the performance, durability, and reliability of our internal AI platform
- Promote AI best practices (usage policies, guardrails, and data handling standards) across development teams in\-compliance with company policy
- Participate in AI office hours and lead group discussions
- Lead AI platform architecture discussions relating to model and design tradeoffs
- Join the on\-call rotation to help ensure the high availability and reliability of D\-Wave applications
Required
- Bachelor’s degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent experience
- 5\+ years of experience designing, deploying, operating, and troubleshooting modern cloud\-based and on\-premises infrastructure, DevOps platforms, or SaaS/PaaS environments
- Hands\-on experience building and supporting production AI applications, including LLM applications, AI agents, workflow automation, or generative AI solutions
- Strong understanding of the LLM application stack, including prompt engineering, retrieval\-augmented generation (RAG), embeddings, vector search, reranking, context management, structured outputs, tool use, evaluation, and AI security practices
- Experience with AWS generative AI technologies, including Amazon Bedrock and AgentCore, as well as agentic orchestration frameworks and interoperability protocols such as MCP and ACP
- Experience designing and operating CI/CD pipelines, infrastructure\-as\-code, artifact management, and containerized deployment environments using technologies such as Kubernetes, Terraform, ArgoCD, Artifactory, Jenkins, Git, or equivalent tools
- Strong Linux administration skills, including troubleshooting, log analysis, system diagnostics, SSH, certificates, security fundamentals, and automation using languages such as Python, Go, Groovy, or similar
- Experience integrating monitoring and observability solutions using tools such as Grafana, OpenSearch, Prometheus, InfluxDB, Zabbix, or equivalent technologies
- Experience integrating third\-party services and APIs, including REST\-based integrations, within Linux\-based environments
- Ability to work independently, solve ambiguous technical problems, collaborate across teams, and translate business workflows into scalable technical solutions
A D\-Waver's DNA
- We look at the future and say “why not”; we see possibilities where others see problems or routines. We show the way ahead and are committed to achieving ambitious goals.
- We practice straight talk and listen generously to each other with empathy. We value different opinions and points of views. We ensure that we connect outside as well as inside to learn from others and inspire each other.
- We hold ourselves accountable for delivering results. We make decisions \& take responsibility so that we can act \& support each other.
- As leaders we motivate \& engage our teams to undertake beyond what they originally thought possible, by developing our teams \& creating the conditions for people to grow and empower themselves through enabling \& coaching.
Our Compensation Philosophy is Simple but Powerful:
We believe providing D\-Wavers with company ownership, competitive pay, and a range of meaningful benefits is the start of creating a culture where people want to give the best they’ve got — not because they’re simply making money, but because they’ve fallen in love with our vision, mission, values, and team.
During the interview process, your Recruiter will review our total rewards (base, equity, bonus, perks, benefit, culture) offerings. The final offer is determined by your proficiencies within this level.
Inclusion:
We celebrate diverse perspectives to drive innovation in our pursuit. Our employees range from distinguished domain experts with decades of experience in their respective fields, to bright and motivated graduates eager to make their mark. Our diverse and innovative team will make you feel appreciated, supported and empower your career growth at D\-Wave.
The Fine Print:
No 3rd party candidates will be accepted
*It is D\-Wave policy to provide equal employment opportunity (EEO) to all persons regardless of race, color, religion, sex, national origin, age, sexual orientation, gender identity, genetic information, physical or mental disability, protected veteran status, or any other characteristic protected by federal, state/provincial, local law.*
The base pay range for this role is:
$150,000 \- $206,000 CAD per year
$150,000 \- $206,000 USD per year
Salary Context
This $150K-$206K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At D-Wave, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($178K) sits 17% below the category median. Disclosed range: $150K to $206K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
D-Wave AI Hiring
D-Wave has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $206K - $230K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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