Staff Machine Learning Research Developer

About the role

D-Wave is seekingaStaff Machine Learning Research Developerto workalongsideour researchers, solutions architects,and software developers specializing invarious domains(e.g.,combinatorialoptimization,graph theory,and quantumphysics).

As a seniormember of theMachineLearningDevelopment team,you will have the opportunity to influence our product offerings. You willleadthearchitecturaldesignand development ofour softwareto enableresearchersand solutions architectstorapidly prototype andexperiment with quantum machine learning methods.Inparallel, you will research and developmachine learningmethodsexploiting the optimization,sampling, and quantum simulationcapabilities of quantum computers.

We are looking forintrinsically motivated individualswho want tomaketechnological and tangibleimpactsat the intersection of quantum computing and machine learning.

What you'll do

• Help the team alignonbest practices for machine learning systemsand infrastructures, research, and products
• Design and developsoftwareformachine learningmethodsusing annealing quantum computers
• Research and develop machine learningmethodsexploiting optimization,sampling, and quantum simulationcapabilities of annealing quantum computers
• Communicate with leadershiptoidentifyquantum machine learning opportunities
• Consistently and comprehensivelydocumentresearch findings forpotentialpublicationsandforbuilding D-Wave’s internal knowledge base
• Clearlyand effectively communicate research findingsand insightstoother D-Wave teams
• Influenceand guide thequantum machine learningroadmapby providing technical feedback toleadership
• Lead and deliver goals on the quantum machine learning roadmap
• Quickly digest research papers, reproduce results, and prototypeand developnovelquantum machine learningmethods

What you'll bring

• 6+ years of professional experienceindevelopingdeeplearning models
• An advanced degree (MS/PhD) in a STEM field, or added years of deep industry experience
• Algorithmic reasoningshould be second nature (e.g.,data structuresand computational complexity)
• Ability toquickly digestresearch papers and implement methods
• Abreadth of knowledge ingenerativemachine learning paradigms(e.g.,energy-based models,flow-basedmodels,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,paralleltemperingand sequential Monte Carlo)
• A solid understanding ofBoltzmann Machines(i.e.,Ising models,Markovrandomfields,exponentialfamily distributions)
• Familiarity with probabilistic graphical models
• Familiarity with annealingand gate-basedquantum computers
• Expertisewith C++ orotherlow-level programming languages
• Contributions to open-source software
• Familiarity withMLOpsecosystems (e.g., Kubeflow,VertexAI, Airflow)
• Experience indelivering end-to-end software projects---from architectto deployment
• Expertisein building extensible APIs and frameworks aroundPyTorch(or, e.g., JAX and TensorFlow)

A D-Waver's DNA