Machine Learning Engineer (ML Engineer)
About the Job
Step into a hands-on Machine Learning Engineering role where youll own production ML delivery end-to-endfrom model development to deployment and monitoringwhile also strengthening the data and software foundations behind mission-critical AI applications. This is a hybrid role with a 40-hour work week and flexible start/end times (core hours 10am4pm) with work in U.S. government secure facilities (SCIFs) as needed.
Whats in it for you
$150000 $215000 salary
Equity: 5000 shares
Hybrid work policy with flexibility (core hours 10am4pm)
Work that directly impacts government/defense mission outcomes
Clear runway for senior engineering growth and technical leadership
Occasional travel to headquarters or U.S. customer sites (as needed)
Visa sponsorship not available
What Youll Be Doing:
Lead platform upgrades to keep AI capabilities modern reliable and scalable
Build and maintain interactive dashboards using a Python SDK turning complex data into decision-ready visuals
Optimize data access patterns to improve throughput latency and system efficiency
Diagnose and resolve runtime performance bottlenecks across services and workflows
Engineer robust scalable applications designed for real-world usage and long-term growth
Deliver production-grade ML systems across the full lifecycle: develop deploy monitor
Partner closely with Technical Product Managers to drive usability improvements and adoption
Apply strong software engineering practices (testing modular design repo structure deployment hygiene)
What Youll Need:
Active U.S. security clearance at TS/SCI TS or Secret or the readiness/eligibility to obtain and maintain one
4 years of experience deploying security-conscious ML solutions and/or data engineering systems in production
Proven ownership of the end-to-end ML lifecycle (development deployment monitoring)
Strong Python proficiency and data/ML ecosystem experience (e.g. pandas numpy scikit-learn PyTorch Plotly)
Production software engineering skills (e.g. Docker Git and modern service/app development practices)
Experience working with large-scale data/compute and performance optimization
Strong written and verbal communication for cross-functional delivery
Bachelors degree in Computer Science (or closely related field) from a top-100 school (as defined by your academic programs ranking)
Ready to Make an Impact
Build applied AI systems that perform under real constraintsand help deliver tools people rely on for critical decisions.
Machine Learning Engineer (ML Engineer)About the Job Step into a hands-on Machine Learning Engineering role where youll own production ML delivery end-to-endfrom model development to deployment and monitoringwhile also strengthening the data and software foundations behind mission-critical AI applic...
Machine Learning Engineer (ML Engineer)
About the Job
Step into a hands-on Machine Learning Engineering role where youll own production ML delivery end-to-endfrom model development to deployment and monitoringwhile also strengthening the data and software foundations behind mission-critical AI applications. This is a hybrid role with a 40-hour work week and flexible start/end times (core hours 10am4pm) with work in U.S. government secure facilities (SCIFs) as needed.
Whats in it for you
$150000 $215000 salary
Equity: 5000 shares
Hybrid work policy with flexibility (core hours 10am4pm)
Work that directly impacts government/defense mission outcomes
Clear runway for senior engineering growth and technical leadership
Occasional travel to headquarters or U.S. customer sites (as needed)
Visa sponsorship not available
What Youll Be Doing:
Lead platform upgrades to keep AI capabilities modern reliable and scalable
Build and maintain interactive dashboards using a Python SDK turning complex data into decision-ready visuals
Optimize data access patterns to improve throughput latency and system efficiency
Diagnose and resolve runtime performance bottlenecks across services and workflows
Engineer robust scalable applications designed for real-world usage and long-term growth
Deliver production-grade ML systems across the full lifecycle: develop deploy monitor
Partner closely with Technical Product Managers to drive usability improvements and adoption
Apply strong software engineering practices (testing modular design repo structure deployment hygiene)
What Youll Need:
Active U.S. security clearance at TS/SCI TS or Secret or the readiness/eligibility to obtain and maintain one
4 years of experience deploying security-conscious ML solutions and/or data engineering systems in production
Proven ownership of the end-to-end ML lifecycle (development deployment monitoring)
Strong Python proficiency and data/ML ecosystem experience (e.g. pandas numpy scikit-learn PyTorch Plotly)
Production software engineering skills (e.g. Docker Git and modern service/app development practices)
Experience working with large-scale data/compute and performance optimization
Strong written and verbal communication for cross-functional delivery
Bachelors degree in Computer Science (or closely related field) from a top-100 school (as defined by your academic programs ranking)
Ready to Make an Impact
Build applied AI systems that perform under real constraintsand help deliver tools people rely on for critical decisions.
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