Interested in this AI/ML Engineer role at D-Wave?
Apply Now →Skills & Technologies
About This Role
D\-Wave (NYSE: QBTS), D\-Wave is a leader in the development and delivery of quantum computing systems, software, and services. We are the world’s first commercial supplier of quantum computers, and the only company building both annealing and gate\-model quantum computers. Our mission is to help customers realize the value of quantum, today. Our quantum computers — the world’s largest — feature QPUs with sub\-second response times and can be deployed on\-premises or accessed through our quantum cloud service, which offers 99\.9% availability and uptime. More than 100 organizations trust D\-Wave with their toughest computational challenges. With over 200 million problems submitted to our quantum systems to date, our customers apply our technology to address use cases spanning optimization, artificial intelligence, research and more. Learn more about realizing the value of quantum computing today and how we’re shaping the quantum\-driven industrial and societal advancements of tomorrow: www.dwavequantum.com.
You can read more about our company and our innovations in the pages of The Wall Street Journal, Time Magazine, Fast Company, MIT Technology Review, Forbes, Inc. Magazine, Wired and across many whitepapers.
At D\-Wave, we’re helping customers realize the value of quantum computing today and are shaping the quantum\-driven industrial and societal advancements of tomorrow.
About the role
D\-Wave is seeking a Staff Machine Learning Research Developer to work alongside our researchers, solutions architects, and software developers specializing in various domains (e.g., combinatorial optimization, graph theory, and quantum physics).
As a senior member of the Machine Learning Development team, you will have the opportunity to influence our product offerings. You will lead the architectural design and development of our software to enable researchers and solutions architects to rapidly prototype and experiment with quantum machine learning methods. In parallel, you will research and develop machine learning methods exploiting the optimization, sampling, and quantum simulation capabilities of quantum computers.
We are looking for intrinsically motivated individuals who want to make technological and tangible impacts at the intersection of quantum computing and machine learning.
What you'll do
- Help the team align on best practices for machine learning systems and infrastructures, research, and products
- Design and develop software for machine learning methods using annealing quantum computers
- Research and develop machine learning methods exploiting optimization, sampling, and quantum simulation capabilities of annealing quantum computers
- Communicate with leadership to identify quantum machine learning opportunities
- Consistently and comprehensively document research findings for potential publications and for building D\-Wave’s internal knowledge base
- Clearly and effectively communicate research findings and insights to other D\-Wave teams
- Influence and guide the quantum machine learning roadmap by providing technical feedback to leadership
- Lead and deliver goals on the quantum machine learning roadmap
- Quickly digest research papers, reproduce results, and prototype and develop novel quantum machine learning methods
What you'll bring
- 6\+ years of professional experience in developing deep learning models
- An advanced degree (MS/PhD) in a STEM field, or added years of deep industry experience
- Algorithmic reasoning should be second nature (e.g., data structures and computational complexity)
- Ability to quickly digest research papers and implement methods
- A breadth of knowledge in generative machine learning paradigms (e.g., energy\-based models, flow\-based models, autoregressive models) complemented by a depth of knowledge in several subdomains
- Strong problem\-solving, communication, and collaboration skills
Nice to have
- Familiarity with Monte Carlo methods (e.g., Metropolis\-Hastings, Gibbs, parallel tempering and sequential Monte Carlo)
- A solid understanding of Boltzmann Machines (i.e., Ising models, Markov random fields, exponential family distributions)
- Familiarity with probabilistic graphical models
- Familiarity with annealing and gate\-based quantum computers
- Expertise with C\+\+ or other low\-level programming languages
- Contributions to open\-source software
- Familiarity with MLOps ecosystems (e.g., Kubeflow, VertexAI, Airflow)
Experience in delivering end\-to\-end software projects* \-from architect to deployment
- Expertise in building extensible APIs and frameworks around PyTorch (or, e.g., JAX and TensorFlow)
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 Systems Inc. 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:
$146,182 \- $219,273 CAD per year
$167,000 \- $230,000 USD per year
Salary Context
This $146K-$230K 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 ($188K) sits 12% below the category median. Disclosed range: $146K to $230K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.