We are seeking an AI/ML Engineer todevelop and maintain autonomous planning, scheduling,and optimizationsystems foradvancedEarth Observationsatelliteoperations.  This role focuses on applying reinforcement learning (RL), operations research, andsequentialdecision-makingtechniques to optimize heterogenous satellite constellation collection plans.   You will be joining an onsite team located in the Herndon, VA office with core in-office days on Tuesday, Wednesday, and Thursdays.  Other days may occasionally be required to support customer or mission-related activities. What You’ll Do  • Design and implement scalable reinforcement learning (RL), optimization, and decision-making algorithms for satellite sensor and constellation tasking and planning  • Build high-fidelity simulation and evaluation environments for training and validating autonomous planning strategies under real-world operational constraints  • Develop multi-objective optimization pipelines balancing coverage, revisit rate, latency, resource utilization, revenue, and mission success metrics  • Train, evaluate, and deploy ML and decision-making models in production environments using modern DevOps practices  • Collaborate with aerospace engineers, mission operators, software engineers, and product teams to translate mission requirements into deployable AI systems    What Success Looks Like (12–18 Months)  • Your modernized scheduling and decision-support system is actively used by planners in daily operations   • Teams can evaluate alternative planning strategies with measurable outcomes based on your models  • Early-stage learning systems (optimization / RL) are improving planning performance over time  Minimum Qualifications  • Bachelor’s degree in Computer Science, Data Science, Aerospace Engineering, Applied Mathematics, Physics, or related field   • 5+ years of experience developing machine learning or optimization systems  • Strong programming skills with experience usingmodern ML frameworks such asPyTorch, TensorFlow, Scikit-learn, or JAX • Experience with probabilistic modeling, uncertainty estimation,andBayesian optimizationalgorithms • Experience building training & evaluation pipelines for ML systems Preferred Qualifications  • Experience with orbital mechanics, satellite systems, remote sensing, mission operations, and collection planning  • Strong software engineering fundamentals including testing, CI/CD, version-control, and containerized deployment  • Familiarity with GPU acceleration and distributed training infrastructure  • Experience with autonomous systems or multi-agent planning architectures is a plus     Pay Transparency: To support pay transparency, Vantor includes salary ranges in all U.S. job postings. Starting pay for this role will fall within the listed range and will be based on factors such as experience, qualifications, skills, location, and market conditions. Candidates who meet the minimum requirements for the role should not expect to receive compensation at the top of the range. The listed range reflects the expected pay for this position, and final offers will be determined based on each candidate’s experience, expertise, and alignment with the role. ● The base pay for this position within the Washington, DC metropolitan area is: $137,000.00 - $182,000.00 - $200,200.00 annually. For all other states, we use geographic cost of labor as an input to develop market-driven ranges for our roles, and as such, each location where we hire may have a different range. Benefits: Vantor offers a competitive total rewards package that goes beyond the standard, including a robust 401(k) with company match, mental health resources, and unique perks like student loan repayment assistance, adoption reimbursement and pet insurance to support all aspects of your life. You can find more information on our benefits at: https://www.Vantor.com/careers Additionally, this position is incentive eligible with a target based on contribution, company performance, and/or individual results achieved; the specific incentive plan and target amount will be determined based on the role and breadth of contributions. The application window is three days from the date the job is posted and will remain posted until a qualified candidate has been identified for hire.  If the job is reposted regardless of reason, it will remain posted three days from the date the job is reposted and will remain reposted until a qualified candidate has been identified for hire.  The date of posting can be found on Vantor's Career page at the top of each job posting. To apply, submit your application via Vantor's Career page.