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<h1 class="section-header section-header--large font-primary" style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Senior ML Ops Engineer</h1>
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<div class="job__location" style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><svg class="svg-icon" fill="none" height="20" style="outline-offset: 2px;" viewbox="0 0 24 24" width="20" xmlns="http://www.w3.org/2000/svg"><path class="icon--primary-color" d="M6.46958 3.811C7.93571 2.34217 9.92464 1.51493 12 1.5108C14.0742 1.51589 16.0619 2.34321 17.5272 3.81142C18.9925 5.27962 19.8158 7.26891 19.8168 9.3432C19.8168 13.3404 15.9936 18.5304 13.9368 21.3192C13.8837 21.3917 13.8319 21.4624 13.7815 21.5311C13.6111 21.7637 13.457 21.9739 13.3236 22.1592C13.1956 22.3393 13.0267 22.4865 12.8308 22.5887C12.635 22.6909 12.4177 22.7452 12.1968 22.7472H12.1776C11.96 22.7479 11.7453 22.6978 11.5505 22.601C11.3558 22.5041 11.1863 22.3631 11.0556 22.1892C10.9602 22.0626 10.8535 21.9241 10.7381 21.7742C10.6981 21.7223 10.6571 21.669 10.6152 21.6144L10.6086 21.6059C8.51808 18.881 4.17957 13.2259 4.17957 9.3456C4.17988 7.27027 5.00345 5.27983 6.46958 3.811ZM12.5796 20.7408L12.7776 20.4708V20.466C14.5968 18 18.3804 12.87 18.3852 9.3504C18.3852 7.65822 17.7129 6.03534 16.5164 4.83878C15.3198 3.64222 13.697 2.97 12.0048 2.97C10.3126 2.97 8.6897 3.64222 7.49314 4.83878C6.29658 6.03534 5.62437 7.65822 5.62437 9.3504C5.62437 12.7426 9.91966 18.3405 11.7581 20.7364L11.7624 20.742V20.748C11.8106 20.8109 11.8727 20.8618 11.9439 20.8966C12.0152 20.9314 12.0935 20.9491 12.1728 20.9484C12.252 20.9477 12.33 20.9286 12.4006 20.8926C12.4712 20.8565 12.5325 20.8046 12.5796 20.7408ZM10.3983 6.23337C11.0137 5.8744 11.7244 5.71293 12.4344 5.77077C13.257 5.83777 14.029 6.19495 14.6126 6.77855C15.1962 7.36214 15.5534 8.13416 15.6204 8.95677C15.6782 9.6668 15.5168 10.3775 15.1578 10.9928C14.7988 11.6081 14.2597 12.0985 13.6131 12.3976C12.9666 12.6968 12.2438 12.7903 11.5424 12.6655C10.8411 12.5407 10.1949 12.2037 9.69114 11.7C9.18741 11.1963 8.85041 10.5501 8.72566 9.84871C8.6009 9.14734 8.6944 8.42457 8.99353 7.77803C9.29266 7.13149 9.78301 6.59233 10.3983 6.23337ZM11.1898 11.0395C11.5657 11.2405 11.995 11.3187 12.4176 11.2632C12.863 11.2046 13.2766 11.0007 13.5943 10.6831C13.9119 10.3654 14.1159 9.95177 14.1744 9.50637C14.2299 9.0838 14.1517 8.65444 13.9507 8.2786C13.7498 7.90275 13.4361 7.5993 13.0538 7.41083C12.6716 7.22237 12.2399 7.15837 11.8194 7.22782C11.3989 7.29728 11.0107 7.49669 10.7093 7.79806C10.4079 8.09943 10.2085 8.48763 10.139 8.90813C10.0696 9.32864 10.1336 9.76034 10.3221 10.1426C10.5105 10.5249 10.814 10.8385 11.1898 11.0395Z"></path></svg><div style="outline-offset: 2px;">Ontario</div></div>
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<p style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Our <a href="https://www.greenhouse.com/mission" style="outline-offset: 2px;" target="_blank">mission</a> at Greenhouse is to make every company great at hiring – so we go to great lengths to hire great people because we believe that they’re the foundation of our success. At Greenhouse, you’ll join a team that collaborates purposefully, fosters inclusivity, and communicates with transparency and accountability so we can help companies measurably improve the way they hire. </p>
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<p style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Join us to do the best work of your career, solving meaningful problems with remarkable teams.</p>
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<p style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;">Greenhouse is looking for a <strong>Senior ML Ops Engineer</strong> to join our team!</p>
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<p style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">As a senior member of our Applied Machine Learning team, you'll own the critical infrastructure and processes that bring our machine learning models to life. You will be responsible for the full model lifecycle, from working with prototypes of ML and LLM-based solutions to transforming them into reliable, production-ready systems. You'll create, manage, and improve our continuous integration and delivery pipelines that enable rapid iteration and deployment. </p>
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<p style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;">Learn more about our engineering culture<a href="https://engineering.greenhouse.io"> here</a>!</p>
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<h3 style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><strong>Who will love this job</strong></h3>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><strong>An ML Evangelist</strong>– You stay in the know of ML industry trends and enable your team’s ability to work with ML tools</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><strong data-id="60340">An Architect</strong> – You excel at creating, managing, and improving robust continuous integration and delivery pipelines to support rapid deployment of ML models</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><strong>A Reliability Advocate</strong> – You are passionate about system uptime, implementing observability practices like monitoring, logging, and alerting, and ensuring high data quality and performance in production</li>
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<li style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;"><strong>A Collaborator</strong> – You excel at working closely with others across an org to align on requirements and deliver solutions</li>
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<h3 style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><strong>What you'll do</strong></h3>
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<li style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;">Operationalize ML and LLM-based workloads, gather stakeholder feedback, and drive them into production-ready systems</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Maintain and improve the infrastructure for ML/LLM evaluation sets, including versioning, automated validation pipelines, and continuous quality checks across the model lifecycle</li>
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<li style="outline-offset: 2px;">Create, manage, and improve continuous integration and delivery pipelines to support rapid iteration and deployment of ML models and services</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Implement observability practices for ML systems, including monitoring, logging, and alerting</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Work closely with ML engineers, data scientists, and cross-functional teams to build impactful solutions</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Ensure high data quality and monitor performance of ML workloads in production</li>
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<li style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;">Participate in an on-call rotation to ensure system uptime and reliability</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Additional projects and responsibilities as business needs require</li>
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<h3 style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><strong>You should have</strong></h3>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Proven experience in an MLOps, DevOps, or a related software engineering field</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Experience implementing safe, ethical, and compliant ML systems (familiarity with ISO 42001/NIST AI RMF and the associated common controls)</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Strong cloud infrastructure experience with AWS</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">A deep understanding of Kubernetes</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Expertise with IaC (Infrastructure as Code) &amp; GitOps Tools like Terraform and Argo CD</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Experience developing and augmenting CI/CD Pipelines</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">A strong understanding of the state of the art in machine learning, especially LLMs</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Familiarity with ML frameworks such as PyTorch, MLFlow, vLLM, Transformers, and Torch</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Practical experience managing data quality and performance in production ML environments</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Experience designing data architectures optimized for AI/ML, such as with Opensearch (vector databases)</li>
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<li data-id="11020" style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Familiarity with tools and platforms like Bedrock/Sagemaker, MetaFlow, Databricks, Vertex AI, Skypilot, Kubeflow, Loki, or Grafana is a plus</li>
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<li style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Your own unique talents! If you don't meet 100% of the qualifications outlined above, tell us why you'd be a great fit for this role in your cover letter</li>
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<em style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">Applicants must be legally eligible to work in Canada as of the start date chosen by the Company. We are unable to support sponsorship at this time.</em>
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<span style="font-weight: 400; outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">For purposes of processing or administering your employment relationship, personal information that you provide to the Company may be transferred to and accessed by an affiliate in the United States or elsewhere, or to agents and contractors (such as payroll companies, insurance companies, information technology consultants, etc.) that provide services to the Company.</span>
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<span style="font-weight: 400; outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">The national pay range for this role is $143,000 - $214,000 CAD. Individual compensation will be commensurate with the candidate's experience and qualifications. Certain roles may be eligible for additional compensation, including stock option awards, bonuses, and merit increases. Additionally, certain roles have the opportunity to receive sales commissions that are based on the terms of the sales commission plan applicable to the role. </span>
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<p style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;"><em><span style="font-weight: 400; outline-offset: 2px;">The anticipated closing date for this role is December 12th, 2025.</span></em></p>
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<p style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;"><em><span style="font-weight: 400;">#LI-WR1</span></em></p>
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<span style="font-weight: 400; outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">This hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.</span>
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<h2 class="auto-8430" style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><strong>Who we are</strong></h2>
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<p style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;">At Greenhouse, we live by our mission through <a href="https://www.greenhouse.com/interviewing-at-greenhouse" style="outline-offset: 2px;" target="_blank">using our own product</a> to help us hire the right person for the job, every time. We are a remote-first company and have shared office spaces in New York City and Ireland, and optional co-working spaces that give us flexibility to do our best work anywhere. We take an active role in our growth through a performance review program that’s committed to providing actionable feedback, and a bonus structure that rewards great performance. We believe that bringing together a variety of perspectives makes us a stronger company – and we nurture leaders who create an inclusive culture and invest into employee resource groups that celebrate our differences and life experiences. We’re proud to have built an <a href="https://www.greenhouse.com/awards" target="_blank">award-winning culture</a> that’s been recognized as Fortune’s Best Places to Work and Inc.’s Best Workplaces multiple years in a row. </p>
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<p style="outline-offset: 2px; outline: rgb(74, 144, 226) solid 3px;"><em style="outline-offset: 2px;">Greenhouse Software is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to accommodations@greenhouse.io.</em></p>
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<em style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;">Emails about job opportunities at Greenhouse Software are only offered by employees with @greenhouse.io email addresses. See </em>
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<p style="outline: rgb(74, 144, 226) solid 3px; outline-offset: 2px;"><em aria-hidden="true" class="auto-5871" style="outline-offset: 2px; background-color: rgba(16, 185, 129, 0.1);">Emails about job opportunities at Greenhouse Software are only offered by employees with @greenhouse.io email addresses. See </em><a href="https://www.greenhouse.com/candidate-phishing-scams" target="_blank"><em>this page</em></a><em> on our website if you suspect a phishing scam.</em></p>

Return a JSON object with these fields: type, title, company, location, department, employment_type, description
user
{
  "type": "job_posting",
  "title": "Senior ML Ops Engineer",
  "company": "Greenhouse",
  "location": "Ontario",
  "department": "Applied Machine Learning Team",
  "employment_type": "TODO",
  "description": "Our mission at Greenhouse is to make every company great at hiring – so we go to great lengths to hire great people because we believe that they’re the foundation of our success. At Greenhouse, you’ll join a team that collaborates purposefully, fosters inclusivity, and communicates with transparency and accountability so we can help companies measurably improve the way they hire. \n\nJoin us to do the best work of your career, solving meaningful problems with remarkable teams.\n\nGreenhouse is looking for a Senior ML Ops Engineer to join our team!\n\nAs a senior member of our Applied Machine Learning team, you'll own the critical infrastructure and processes that bring our machine learning models to life. You will be responsible for the full model lifecycle, from working with prototypes of ML and LLM-based solutions to transforming them into reliable, production-ready systems. You'll create, manage, and improve our continuous integration and delivery pipelines that enable rapid iteration and deployment. \n\nLearn more about our engineering culture here!\n\nWho will love this job\nAn ML Evangelist– You stay in the know of ML industry trends and enable your team’s ability to work with ML tools\nAn Architect – You excel at creating, managing, and improving robust continuous integration and delivery pipelines to support rapid deployment of ML models\nA Reliability Advocate – You are passionate about system uptime, implementing observability practices like monitoring, logging, and alerting, and ensuring high data quality and performance in production\nA Collaborator – You excel at working closely with others across an org to align on requirements and deliver solutions\nWhat you'll do\nOperationalize ML and LLM-based workloads, gather stakeholder feedback, and drive them into production-ready systems\nCreate, manage, and improve continuous integration and delivery pipelines to support rapid iteration and deployment of ML models and services\nImplement observability practices for ML systems, including monitoring, logging, and alerting\nMaintain and improve the infrastructure for ML/LLM evaluation sets, including versioning, automated validation pipelines, and continuous quality checks across the model lifecycle\nEnsure high data quality and monitor performance of ML workloads in production\nWork closely with ML engineers, data scientists, and cross-functional teams to build impactful solutions\nParticipate in an on-call rotation to ensure system uptime and reliability\nAdditional projects and responsibilities as business needs require\nYou should have\nProven experience in an MLOps, DevOps, or a related software engineering field\nExperience implementing safe, ethical, and compliant ML systems (familiarity with ISO 42001/NIST AI RMF and the associated common controls)\nStrong cloud infrastructure experience with AWS\nA deep understanding of Kubernetes\nExpertise with IaC (Infrastructure as Code) & GitOps Tools like Terraform and Argo CD\nExperience developing and augmenting CI/CD Pipelines\nA strong understanding of the state of the art in machine learning, especially LLMs\nFamiliarity with ML frameworks such as PyTorch, MLFlow, vLLM, Transformers, and Torch\nPractical experience managing data quality and performance in production ML environments\nExperience designing data architectures optimized for AI/ML, such as with Opensearch (vector databases)\nFamiliarity with tools and platforms like Bedrock/Sagemaker, MetaFlow, Databricks, Vertex AI, Skypilot, Kubeflow, Loki, or Grafana is a plus\nYour own unique talents! If you don't meet 100% of the qualifications outlined above, tell us why you'd be a great fit for this role in your cover letter\nApplicants must be legally eligible to work in Canada as of the start date chosen by the Company. We are unable to support sponsorship at this time.\n\nFor purposes of processing or administering your employment relationship, personal information that you provide to the Company may be transferred to and accessed by an affiliate in the United States or elsewhere, or to agents and contractors (such as payroll companies, insurance companies, information technology consultants, etc.) that provide services to the Company.\n\nThe national pay range for this role is $143,000 - $214,000 CAD. Individual compensation will be commensurate with the candidate's experience and qualifications. Certain roles may be eligible for additional compensation, including stock option awards, bonuses, and merit increases. Additionally, certain roles have the opportunity to receive sales commissions that are based on the terms of the sales commission plan applicable to the role. \n\nThe anticipated closing date for this role is December 12th, 2025.\n\n#LI-WR1\n\nThis hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.\n\nWho we are\nAt Greenhouse, we live by our mission through using our own product to help us hire the right person for the job, every time. We are a remote-first company and have shared office spaces in New York City and Ireland, and optional co-working spaces that give us flexibility to do our best work anywhere. We take an active role in our growth through a performance review program that’s committed to providing actionable feedback, and a bonus structure that rewards great performance. We believe that bringing together a variety of perspectives makes us a stronger company – and we nurture leaders who create an inclusive culture and invest into employee resource groups that celebrate our differences and life experiences. We’re proud to have built an award-winning culture that’s been recognized as Fortune’s Best Places to Work and Inc.’s Best Workplaces multiple years in a row. \n\n– \n\nGreenhouse Software is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to accommodations@greenhouse.io.\n\nEmails about job opportunities at Greenhouse Software are only offered by employees with @greenhouse.io email addresses. See this page on our website if you suspect a phishing scam.\n\n"
}
assistant