Ensure the safety and security of our nation as a ML/OPs Engineer at GDIT, deploying, fine‑tuning, and monitoring production machine learning models and supporting data extraction, transformation, and load processes.
Locations (2)
McLean, Virginia, United States
Tysons Corner, Virginia, United States
Compensation
The likely salary range for this position is $187,179 - $230,000.
Responsibilities
Deploy, fine‑tune, and monitor production machine learning models
Provide support in ETL, data mapping, analytics, operations, databases, and maintenance of data systems
Manage containerization and orchestration of ML services (Docker, Kubernetes)
Build and maintain CI/CD pipelines for machine learning models and data workflows
Collaborate with data scientists, data engineers, and software developers to operational ML solutions
Build and maintain backend systems that power agent evaluation and continuous improvement, design data models, develop enterprise integrations, own features end to end, work directly with customers, debug distributed workflows and improve system reliability.
Compensation
Base salary is $120,000 to $180,000 USD annually. Visa sponsorship is available, including H-1B transfers, OPT, and O-1.
Responsibilities
Build and maintain backend systems that power agent evaluation and continuous improvement.
Design data models for evaluation datasets, experiment runs, agent traces, knowledge versions, findings, and customer-defined quality criteria.
Develop and support enterprise integrations, including authentication, OAuth, webhooks, incremental syncs, backfills, rate limits, retries, and monitoring.
Own features end to end, from technical design and implementation through deployment, observability, customer feedback, and iteration.
Work directly with customers to diagnose system failures and turn individual needs into reusable product capabilities.
Senior ML Engineer (MSc. or PhD) contributing to AI training projects involving model development, training and inference systems, numerical computing, performance optimization, and Python.
Compensation
Pay: $60–$120/hour
Responsibilities
Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
Implement model components, data pipelines, evaluation systems, and numerical methods.
Build reproducible programmatic workflows using Python and command-line tools.
Requirements
Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
Lead modeling and simulation activities to design and validate refrigeration systems using advanced simulation software and state-of-the-art testing equipment.
Compensation
We offer competitive compensation and comprehensive benefits and programs.
Responsibilities
Lead modeling and simulation activities in alignment with the company’s product development process, ensuring high-quality system analysis, risk identification, and actionable design recommendations.
Define and drive the technical roadmap for 1D system simulation applications, improving simulation capability through new technologies, enhanced methodologies, and practical engineering needs.
Develop, review, and refine component mathematical models, system-level architectures, and unit control logic characteristics, ensuring accurate functional implementation and strong correlation with physical behavior.
Lead the integration of simulation with experimental validation, using test data to calibrate models, improve prediction accuracy, and support design decisions.
Drive standardization, automation, and continuous improvement of refrigeration system design and simulation workflows to increase development efficiency and technical consistency.
Conduct research in remote sensing, simulation, and AI/ML to support NASA/USGS Landsat mission.
Compensation
$35,000.00 - $90,000.00 Annual
Responsibilities
Conduct independent research including hypothesis construction, study design, data management, model development, experiments, analysis and interpretation
Collaborate with internal and external stakeholders
Ensure compliance with institutional, sponsor, and regulatory requirements
Disseminate and publish research results in peer-reviewed journals, reports, meetings, workshops, and conferences
Assist in writing grant proposals
Requirements
Ph.D. or equivalent terminal degree in a related field
Lead AI technical direction and product integration for aerospace systems.
Compensation
The annual base salary range for this position is $214,000– 268,000.
Responsibilities
Lead the application of AI, machine learning, generative AI, and agentic AI technologies to solve complex product, engineering, and operational challenges.
Establish and promote AI development tools, agentic frameworks, workflows, and best practices that enable rapid experimentation, prototyping, and deployment.
Partner with engineering, product, and research teams to identify and execute high-impact opportunities for AI-enabled product innovation.
Design, develop, and integrate AI-driven capabilities into next-generation navigation, sensing, autonomy, and aerospace systems.
Provide technical leadership in AI architectures, model development, data strategies, and agent-based system design across Advanced Projects initiatives.
Requirements
Advanced degree (M.S. or Ph.D.) in Computer Science, Artificial Intelligence, Aerospace Engineering, Electrical Engineering, Robotics, Applied Mathematics, Physics, or related technical field
Nice to have: large language models (LLMs)
Nice to have: retrieval-augmented generation (RAG)
Product Manager for Global Revenue Growth Management (RGM) AI product at Coca‑Cola, responsible for vision, roadmap, discovery, requirements, governance, and adoption across 200+ markets.
Compensation
United States: 202,000 - 229,000 USD
Responsibilities
Own AI product vision and roadmap
Conduct discovery with RGM teams
Translate discovery into AI requirements
Prioritize backlog and define success metrics
Design governance and guardrails
Requirements
Bachelor’s degree in Business, Economics, Computer Science, Engineering, or related field; MBA or technical postgraduate degree preferred
Nice to have: B2B or enterprise platform experience
Nice to have: RGM, pricing, or commercial experience at a CPG company
Nice to have: Direct experience in Engineering, Data Science, Architecture
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