Kiavi is one of the nation’s largest private lenders to residential real estate investors (REIs). We harness the power of data &amp; technology to offer our customers a simpler, more reliable, and faster way to access the capital they need to scale their businesses.  Formerly known as LendingHome, we’re committed to helping REIs revitalize the approximately $25 trillion worth of aged U.S. housing stock to provide move-in ready homes and rental housing for millions of Americans across the country. Combining our technology and industry expertise, Kiavi has grown to a team of over 400 and has won many awards over the years including Forbes Fintech 50, Finovate Awards, Best Digital Mortgage Platform, Fast Company’s Most Innovative Companies, and <a href="https://www.kiavi.com/news">many more</a>.  <ul> <li>Loans Funded: $27 billion in loans funded</li> <li>Homes financed: 100,000+ projects funded for our customers across the country</li> <li>Geography: 45 states + D.C. where we lend to REIs so they can revitalize neighborhoods</li> <li>Employees: Over 450 employees with competitive benefits and perks</li> </ul> This position can be based remotely in any of our approved hiring regions. Our hybrid distributed workforce philosophy allows us to find the best talent in the country and build diverse teams, while allowing our colleagues to work from the location that works best for them.  We are not approved to hire in the following regions: Alabama, Alaska, Arkansas, Connecticut, Delaware, Idaho, Indiana, Iowa, Louisiana, Massachusetts, Mississippi, Montana, New Hampshire, New Mexico, North Dakota, Oklahoma, South Dakota, Vermont, Wyoming. <hr /> JOB SUMMARY Kiavi is looking for a skilled MLOps Engineer to join our data team.  As an MLOps Engineer, you will be responsible for designing and implementing Machine Learning platform features for our partner Data Scientists. You will work with a variety of models ranging from online or real-time models, models which serve batched results, or large language models using Databricks, MLFlow, Python and related technologies. The ideal candidate should have a strong background in data science and MLOps or DevOps, with experience in designing, building, and optimizing models in non-production and production environments. JOB DUTIES <ul> <li>Experience with online (real-time) and offline (batch) models in large production environments.</li> <li>Develop and maintain machine learning models, CICD solutions, and feature stores.</li> <li>Collaborate with cross-functional teams, including Data Engineering, Data Science and DevOps, to ensure seamless data integration.</li> <li>Participate in code reviews, ensuring code quality and best practices are followed.</li> <li>Implement automated solutions to monitor and optimize data processing systems to ensure they operate efficiently and effectively.</li> <li>Document and communicate data engineering solutions and processes to relevant stakeholders.</li> <li>Work closely with other teams to identify opportunities for automation and process improvement.</li> </ul> MINIMUM QUALIFICATIONS <ul> <li>Bachelors degree in Computer Science, Data Science, or a related field.</li> <li>Experience building and testing ML models.</li> <li>Experience developing and maintaining CICD Pipelines in tools like CircleCI, Jenkins, or BuildKite.</li> <li>Hands-on experience with big data technologies such as Spark and Hive.</li> <li>Advanced expertise in SQL and NoSQL databases.</li> <li>Experience with Airflow or similar workflow orchestrators.</li> <li>Familiarity with AWS, Azure, or GCP concepts.</li> <li>Proven ability and aptitude for learning and applying new technologies in your work. </li> <li>Strong knowledge of data structures, algorithms, and software design principles.</li> <li>Excellent problem-solving skills and attention to detail.</li> <li>Good written and verbal communication skills.</li> <li>Ability to work in a fast-paced, collaborative environment.</li> </ul> PREFERRED QUALIFICATIONS <ul> <li>Experience with Databricks and PySpark.</li> <li>Experience with containerization tools such as Docker or Kubernetes.</li> <li>Maintaining and implementing feature stores for machine learning models</li> </ul> <hr /> Kiavi takes a market-based approach to pay, and pay may vary depending on your location. Work locations are categorized into one of three tiers based on cost of labor for that geographic area. These ranges may be modified in the future. Region 1 - $149,700 - $224,500 + benefits Region 2 - $142,200 - $213,400 + benefits Region 3 - $134,700 - $202,100 + benefits To find a location’s tier, please refer to <a href="https://www.kiavi.com/compensation">https://kiavi.com/compensation</a>. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries. Within the range, the successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. Your recruiter can share more about the specific base pay range for your preferred location during the hiring process. Please note that the pay range listed above reflects the base salary only, and does not include the target value benefits. <hr /> Benefits and Perks  <ul> <li>Medical, Dental, and Vision Insurance: Kiavi offers free employee-only coverage for medical, dental, and vision plans. Dependents receive a heavily discounted premium, competitively placed in the 90th percentile for cost.</li> <li>Health Savings Account (HSA): For those enrolled in a High Deductible Health Plan (HDHP), Kiavi provides an annual employer contribution to HSAs: $500 for individual coverage and $1,000 for dependents.</li> <li>Disability and Life Insurance: Immediate access to short-term and long-term disability insurance, as well as group life and AD&amp;D insurance, with coverage at 1x annual salary.</li> <li>Remote Work Policy: Kiavi supports working remotely for day to day activities for most roles and occasionally may ask employees to gather in person for key meetings, trainings, team building etc. For remote workers, Kiavi offers a one-time office setup reimbursement of up to $500 and a monthly $85 stipend for internet and phone expenses. For in-office workers, Kiavi offers employer contributions to commuter benefits account, either for parking or public transportation.</li> <li>Flexible Time Off: Employees are empowered to manage their own time off, with no strict limits, as long as its approved by their manager.</li> <li>Paid Leaves: Up to 8 weeks of paid leave for medical or caregiver purposes, and 12 weeks of paid maternity/paternity leave. Short-term disability leave is also available for birthing mothers.</li> <li>401(k) &amp; Company Match: A 401(k) plan managed by Fidelity, with Kiavi matching 100% of contributions up to 3% of compensation or $6,000 (whichever is less). All employer contributions are immediately vested. </li> </ul> <hr /> Protect yourself from recruitment scams: <ul> <li>If you are offered an interview or a role with Kiavi, this will be communicated with you through email, via an official email address ending in @kiavi.com or @lendinghome.com. </li> <li>You will never be asked for payments of any kind during the process.</li> <li>We also never communicate with candidates via Whatsapp at any point during the recruitment process. </li> <li>We won’t ask for personal information or data via text message. If you have any concerns regarding how genuine a text message is, please contact your recruiter.</li> <li>You can find our official Kiavi careers <a href="https://www.kiavi.com/careers">page here</a>. We advise you to check that the page details and website addresses match if you have any concerns.</li> </ul> <hr /> <h5>Kiavi is an Equal Opportunity Employer<a href="http://sfgov.org/olse/sites/default/files/FileCenter/Documents/11600-Art%20%2049%20Official%20Notice%20Final%20091114.pdf" target="_blank"> San Francisco Fair Chance Ordinance Police Code, Article 49</a></h5> <h5>We use <a href="https://yellow-bari-48.tiiny.site">E-Verify</a> to confirm the identity and employment eligibility of all new hires.</h5>

Kiavi

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Kiavi

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