Senior Machine Learning Operations Engineer New
Built Technologies
RemoteRemote$140,000 - $210,000 a year
Job Description
About Built
Built's Mission: Connect and simplify doing business in real estate.
Built is the AI-powered platform transforming the way real estate is financed, developed, and managed. Purpose-built for real estate and construction, Built began by fixing construction draw management for lenders and has grown into a comprehensive operating system addressing some of the industry’s most complex challenges.
Through its connected product suite, Built enables stakeholders to finance, develop, build, own, and operate smarter—all in one place. The platform brings together loans, deals, portfolios, payments, inspections, and collaboration to deliver faster execution, greater transparency, efficiency, and trust across the industry.
Today, Built is a partner to more than 350 lenders, over 80,000 borrowers and owners, and thousands of contractors, powering 86,000 active projects valued at more than $300 billion. Learn more at getbuilt.com:
Life At Built / Built Cares
Series D Financing Round
Built Recognized in Two American Business Award Categories
Built Secures Investment from Citi
Senior Machine Learning Operations Engineer
Role Summary & Scope
Built is investing in applied machine learning to power the next generation of data products in construction finance. We’re hiring our first dedicated Senior ML Ops Engineer to build the foundation that makes that possible.
Today, our data scientists are building models. What we don’t yet have is the infrastructure, lifecycle automation, and production standards to reliably deploy and scale them. This role exists to change that.
You’ll design and implement the ML Ops platform that enables training, deployment, monitoring, governance, and automation across our ecosystem. This is a 0 1 build. You’ll define tooling, establish standards, and integrate ML workloads into our AWS-native, event-driven architecture.
This is not a research or modeling role. It’s a platform engineering role focused on productionizing machine learning systems. Your work will directly enable new benchmarking and anonymized data products that expand Built’s market opportunity.
You’ll partner closely with Data Engineering, Data Science, and Platform teams to establish how ML systems operate across Built.
What You’ll Do
You’ll build and operationalize the infrastructure that allows machine learning to run reliably in production.
Specifically, you will:
Architect and implement Built’s foundational ML Ops platform from scratch
Define and deploy reusable patterns for model training, deployment, monitoring, and retraining
Build CI/CD pipelines for ML lifecycle automation, including versioning and experimentation tracking
Stand up a feature store integrated with Snowflake and AWS to support structured and unstructured data
Implement model registry and governance standards to ensure reproducibility, auditability, and rollback capability
Integrate ML workloads into our event-driven architecture (Kafka, Kinesis)
Develop observability frameworks to monitor drift, performance, latency, and model quality in production
Automate ML infrastructure using Terraform and AWS-native tooling (SageMaker, Lambda, ECS, Batch, Step Functions)
Establish security and compliance standards across ML assets, including data lineage and access control
Mentor engineers on ML Ops patterns and deployment best practices
This role is hands-on and foundational. You’ll be shaping how machine learning operates at Built for years to come.
Skills & Experience
We’re looking for a builder - someone who has personally designed and productionized ML infrastructure before.
Must-Have Skills
Experience architecting and deploying ML systems in production environments
Deep familiarity with ML lifecycle automation (training, CI/CD, deployment, monitoring)
Strong AWS experience, particularly within ML pipelines (SageMaker preferred)
Proven experience building infrastructure-as-code solutions (Terraform)
Experience productionizing ML workflows end-to-end, not just optimizing existing systems
Strong Python proficiency
Experience integrating ML workloads with data platforms and event-driven systems
Solid SQL skills and familiarity working with Snowflake
Nice-to-Have Skills
Experience implementing feature stores or model registries
Familiarity with data orchestration tools (Airflow, Prefect, Dagster)
Experience with ML observability tooling (Datadog, Prometheus)
Experience in regulated or financial data environments
Experience optimizing ML workloads for cost and scale
Exposure to Snowpark, Bedrock, or LLM orchestration frameworks
What Will Make You Successful
You’ve built ML infrastructure from the ground up or led a major re-architecture
You’re comfortable working in ambiguity and defining standards where none exist
You think in systems and care about reliability, governance, and scalability
You collaborate well with data scientists and engineers to turn prototypes into production systems
You take ownership and move quickly without sacrificing quality
Built’s salary range for this position is $140,000 - $210,000 USD per year. The pay range is designed to accommodate upward mobility in the role, therefore it encompasses the full span of proficiency levels for this role and we believe that the midpoint of the range is competitive in the market. Salary is just one component of Built's total compensation package for employees. Your total rewards package at Built will include equity, top-notch medical, dental and vision coverage, an unlimited PTO policy, and other benefits.
Travel Requirement: Teammates must be able to travel at minimum twice per year to Nashville, TN or another designated location for company-wide events (e.g., "Connect Week"). Additional travel may be required based on business needs and role responsibilities.
Perks:
The rare opportunity to radically disrupt a $1.5T industry
Competitive benefits including: uncapped vacation, health, dental & vision insurance
401k with match and expedited vesting
Robust compensation package, including equity in the form of stock options
Flexible working hours, paid family leave, ERGs & Mentorship opportunities
Learning grant program to support ongoing professional development
Built brings together passionate people who are driven in a variety of disciplines, each bringing their unique perspective to everything they do.
We’re committed to building a safe, inclusive workplace where every employee can succeed, and we recruit, hire, and promote fairly - without bias based on race, color, religion, sex, sexual orientation, gender identity, marital status, veteran status, or any other characteristic protected by law.
Greenhouse Data Disclosure
When you apply to a job on this site, the personal data contained in your application will be collected by Built Technologies (“Controller”). Your personal data will be processed for the purposes of managing Controller’s recruitment related activities, which include setting up and conducting interviews and tests for applicants, evaluating and assessing the results thereto, and as is otherwise needed in the recruitment and hiring processes. Your personal data will be shared with Greenhouse Software, Inc., a cloud services provider located in the United States of America and engaged by Controller to help manage its recruitment and hiring process on Controller’s behalf. Your personal data will be retained by Controller as long as Controller determines it is necessary to evaluate your application for current or future employment. You have the right to request access to your personal data, to request that your personal data be rectified or erased, and to request that processing of your personal data be restricted. You also have to right to data portability.
If you would like to contact Controller to ask questions surrounding processing of your personal data or to make a request, you can send an email to [email protected]. California residents: Please note that Controller does not sell your personal data or share it with third parties outside of Greenhouse Software, Inc.
Built's Mission: Connect and simplify doing business in real estate.
Built is the AI-powered platform transforming the way real estate is financed, developed, and managed. Purpose-built for real estate and construction, Built began by fixing construction draw management for lenders and has grown into a comprehensive operating system addressing some of the industry’s most complex challenges.
Through its connected product suite, Built enables stakeholders to finance, develop, build, own, and operate smarter—all in one place. The platform brings together loans, deals, portfolios, payments, inspections, and collaboration to deliver faster execution, greater transparency, efficiency, and trust across the industry.
Today, Built is a partner to more than 350 lenders, over 80,000 borrowers and owners, and thousands of contractors, powering 86,000 active projects valued at more than $300 billion. Learn more at getbuilt.com:
Life At Built / Built Cares
Series D Financing Round
Built Recognized in Two American Business Award Categories
Built Secures Investment from Citi
Senior Machine Learning Operations Engineer
Role Summary & Scope
Built is investing in applied machine learning to power the next generation of data products in construction finance. We’re hiring our first dedicated Senior ML Ops Engineer to build the foundation that makes that possible.
Today, our data scientists are building models. What we don’t yet have is the infrastructure, lifecycle automation, and production standards to reliably deploy and scale them. This role exists to change that.
You’ll design and implement the ML Ops platform that enables training, deployment, monitoring, governance, and automation across our ecosystem. This is a 0 1 build. You’ll define tooling, establish standards, and integrate ML workloads into our AWS-native, event-driven architecture.
This is not a research or modeling role. It’s a platform engineering role focused on productionizing machine learning systems. Your work will directly enable new benchmarking and anonymized data products that expand Built’s market opportunity.
You’ll partner closely with Data Engineering, Data Science, and Platform teams to establish how ML systems operate across Built.
What You’ll Do
You’ll build and operationalize the infrastructure that allows machine learning to run reliably in production.
Specifically, you will:
Architect and implement Built’s foundational ML Ops platform from scratch
Define and deploy reusable patterns for model training, deployment, monitoring, and retraining
Build CI/CD pipelines for ML lifecycle automation, including versioning and experimentation tracking
Stand up a feature store integrated with Snowflake and AWS to support structured and unstructured data
Implement model registry and governance standards to ensure reproducibility, auditability, and rollback capability
Integrate ML workloads into our event-driven architecture (Kafka, Kinesis)
Develop observability frameworks to monitor drift, performance, latency, and model quality in production
Automate ML infrastructure using Terraform and AWS-native tooling (SageMaker, Lambda, ECS, Batch, Step Functions)
Establish security and compliance standards across ML assets, including data lineage and access control
Mentor engineers on ML Ops patterns and deployment best practices
This role is hands-on and foundational. You’ll be shaping how machine learning operates at Built for years to come.
Skills & Experience
We’re looking for a builder - someone who has personally designed and productionized ML infrastructure before.
Must-Have Skills
Experience architecting and deploying ML systems in production environments
Deep familiarity with ML lifecycle automation (training, CI/CD, deployment, monitoring)
Strong AWS experience, particularly within ML pipelines (SageMaker preferred)
Proven experience building infrastructure-as-code solutions (Terraform)
Experience productionizing ML workflows end-to-end, not just optimizing existing systems
Strong Python proficiency
Experience integrating ML workloads with data platforms and event-driven systems
Solid SQL skills and familiarity working with Snowflake
Nice-to-Have Skills
Experience implementing feature stores or model registries
Familiarity with data orchestration tools (Airflow, Prefect, Dagster)
Experience with ML observability tooling (Datadog, Prometheus)
Experience in regulated or financial data environments
Experience optimizing ML workloads for cost and scale
Exposure to Snowpark, Bedrock, or LLM orchestration frameworks
What Will Make You Successful
You’ve built ML infrastructure from the ground up or led a major re-architecture
You’re comfortable working in ambiguity and defining standards where none exist
You think in systems and care about reliability, governance, and scalability
You collaborate well with data scientists and engineers to turn prototypes into production systems
You take ownership and move quickly without sacrificing quality
Built’s salary range for this position is $140,000 - $210,000 USD per year. The pay range is designed to accommodate upward mobility in the role, therefore it encompasses the full span of proficiency levels for this role and we believe that the midpoint of the range is competitive in the market. Salary is just one component of Built's total compensation package for employees. Your total rewards package at Built will include equity, top-notch medical, dental and vision coverage, an unlimited PTO policy, and other benefits.
Travel Requirement: Teammates must be able to travel at minimum twice per year to Nashville, TN or another designated location for company-wide events (e.g., "Connect Week"). Additional travel may be required based on business needs and role responsibilities.
Perks:
The rare opportunity to radically disrupt a $1.5T industry
Competitive benefits including: uncapped vacation, health, dental & vision insurance
401k with match and expedited vesting
Robust compensation package, including equity in the form of stock options
Flexible working hours, paid family leave, ERGs & Mentorship opportunities
Learning grant program to support ongoing professional development
Built brings together passionate people who are driven in a variety of disciplines, each bringing their unique perspective to everything they do.
We’re committed to building a safe, inclusive workplace where every employee can succeed, and we recruit, hire, and promote fairly - without bias based on race, color, religion, sex, sexual orientation, gender identity, marital status, veteran status, or any other characteristic protected by law.
Greenhouse Data Disclosure
When you apply to a job on this site, the personal data contained in your application will be collected by Built Technologies (“Controller”). Your personal data will be processed for the purposes of managing Controller’s recruitment related activities, which include setting up and conducting interviews and tests for applicants, evaluating and assessing the results thereto, and as is otherwise needed in the recruitment and hiring processes. Your personal data will be shared with Greenhouse Software, Inc., a cloud services provider located in the United States of America and engaged by Controller to help manage its recruitment and hiring process on Controller’s behalf. Your personal data will be retained by Controller as long as Controller determines it is necessary to evaluate your application for current or future employment. You have the right to request access to your personal data, to request that your personal data be rectified or erased, and to request that processing of your personal data be restricted. You also have to right to data portability.
If you would like to contact Controller to ask questions surrounding processing of your personal data or to make a request, you can send an email to [email protected]. California residents: Please note that Controller does not sell your personal data or share it with third parties outside of Greenhouse Software, Inc.