Full Stack Developer (Python, Java, Docker, Kubernetes)

As a developer for the US Census Bureau, you will design, develop, and maintain the highly complex routing and scheduling algorithms that power the Mojo field operations control system. You will architect modular, scalable optimization services capable of handling both massive batch schedules and real-time, on-demand routing. You will ensure maximum operational efficiency and data-driven accuracy for critical national survey workflows. 

Your Role: 

Optimizer Design, Development & Support 

• Design and implement vehicle routing and scheduling algorithms using OR-Tools to generate optimal field assignments 

• Develop clustering algorithms to group workloads geographically and minimize travel time/cost 

• Implement constraint models (capacity, time windows, skills, priorities, survey rules) within the optimizer 

• Integrate the optimizer with the Mojo control system APIs for job intake, execution, and results publishing 

• Build batch and real-time optimization modes to support both scheduled and on-demand routing 

• Refactor optimizer components into modular, extensible strategy interfaces for new routing heuristics 

• Implement data preprocessing pipelines to normalize locations, distances, and travel matrices 

• Develop automated test harnesses validating solution correctness against known datasets and constraints 

• Implement “solution quality” scoring metrics (distance, balance, SLA adherence, cost) to evaluate goodness of routes 

• Create regression benchmarks comparing new algorithm performance vs. baseline outputs 

• Add parallel processing and scaling support to handle large route sets and high agent counts 

• Instrument services with logging and metrics to track runtime, solver performance, and solution quality 

• Package optimizer services for CI/CD deployment with reproducible builds and environment configs 

• Document routing logic, constraints, integration points, and operational runbooks 

• Provide production support, tuning, and continuous improvement of optimization heuristics and performance 

Requirements

• 5+ years’ experience designing and implementing routing, scheduling, or optimization systems 

• Strong algorithmic background including graph theory, clustering, and heuristic/approximation techniques 

• Proficiency in Python or Java for building optimization services and supporting tooling 

• Experience developing scalable, high-performance services integrated with enterprise control systems (e.g., Mojo) 

• Demonstrated ability to design test harnesses validating both solution correctness and optimization quality 

• Experience implementing performance tuning, parallel processing, and large dataset handling 

• Familiarity with REST APIs, data pipelines, and system integration patterns 

• Experience instrumenting systems with logging, metrics, and monitoring for operational visibility 

• Ability to document algorithms, constraints, and operational procedures and collaborate across engineering and operations teams 

Benefits

• 401(k) with matching and 100% Vested
• Health Insurance - 3 plans to select from
• Dental insurance
• Vision Insurance
• Health savings account
• Life insurance
• Short Term Disability
• Long Term Disability
• AD&D
• Paid time off
• Professional development assistance
• Training
• Tuition reimbursement
• Flexible schedule
• Flexible spending account
• Referral program
• Paid Legal Plan
• and more....

Ignite IT is an Equal Employment Opportunity/Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, religion, sex, national origin, disability, Veteran status, sexual orientation, or other protected characteristic. In accordance with EO 13665 Final Rule, Ignite IT will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.

Applicants selected may be required to possess and maintain a government clearance

US CITIZENSHIP REQUIRED