Job Requirements
Remote McLean, VA Columbia, MD
Clearance Unspecified Polygraph not specified
Mid Level Career (5+ yrs experience)
$140,000 - $180,000
Job Description
ROLE SUMMARY
We’re hiring Machine Learning Engineers to join our growing AI/ML team. You’ll work across the full ML lifecycle — from dataset preparation and model training to production inference and evaluation — for some of the most demanding customer mission sets in the federal space. This role is hands-on and production oriented. You’ll train generative models, build evaluation pipelines, optimize inference performance, and collaborate with product engineers to ship capabilities that matter. We’re looking for engineers who are comfortable working with PyTorch, large-scale GPU training, and who stay current on the rapidly evolving generative AI landscape.
WHAT YOU’LL DO
• Train, fine-tune, and evaluate generative AI models (diffusion models, GANs, LoRA adapters) for photorealistic image and video generation
• Build and maintain training pipelines: dataset curation, preprocessing, augmentation, hyperparameter tuning, and quality benchmarking across GPU clusters
• Develop evaluation frameworks to benchmark model outputs against commercial detection systems, quantifying pass rates and identifying weaknesses
• Optimize inference pipelines for throughput and latency, including model quantization, batching strategies, and GPU memory management
• Integrate open-source models (Flux family, Hugging Face Transformers, ComfyUI) into workflows with appropriate quality controls
• Collaborate with product engineering to deploy models behind production APIs (Django backend, Celery/Dramatiq task queues)
• Contribute to research on adversarial ML techniques, counter-detection methods, and emerging generative architectures
• Document model performance, training procedures, and architectural decisions for team knowledge sharing
REQUIRED QUALIFICATIONS
• 3+ years of professional experience in machine learning engineering, with hands-on generative model experience (diffusion models, GANs, or transformer-based generation)
• Strong proficiency in Python and PyTorch, with experience training models on multi-GPU setups
• Experience with at least one of: LoRA/QLoRA fine-tuning, Stable Diffusion / Flux model family, ComfyUI workflow development, or custom training pipelines
• Understanding of ML fundamentals: loss functions, optimization, regularization, evaluation metrics, and common failure modes
• Experience with data pipeline tools and practices: dataset versioning, preprocessing, and quality validation
• Familiarity with Linux environments, Docker, and cloud compute (AWS preferred)
• Strong debugging and experimentation skills — comfortable iterating on model architectures and training configurations
• Bachelor’s degree in Computer Science, Machine Learning, Mathematics, or a related field (or equivalent professional experience)
• U.S. citizenship (required for work with federal customers)
PREFERRED QUALIFICATIONS
• Experience with distributed training frameworks (Ray, DeepSpeed, PyTorch DDP/FSDP)
• Background in computer vision, image processing, or facial recognition systems
• Experience deploying ML models to production (model serving, API integration, monitoring)
• Familiarity with adversarial machine learning or AI safety research
• Experience with LLM fine-tuning, RAG systems, or agentic AI frameworks (LangChain/LangGraph)
• Publication record in ML/AI venues (NeurIPS, ICML, CVPR, etc.) or meaningful open-source contributions
COMPENSATION & BENEFITS
Comprehensive benefits package designed for high-performing professionals:
• Fully paid medical, dental, and vision insurance premiums
• 10% company contribution to a Vanguard-sponsored 401(k) plan, vested immediately
• 7 weeks paid time off, accrued semi-monthly — use it however you like
• Life, short-term disability, and long-term disability insurance
• Dependent care and health FSA accounts
• $5,000 annual professional development and tuition reimbursement
• $200 monthly communication allowance (phone and internet)
• $2,000 technology allowance every two years
• Employee Equity Plan (Woven Bonus Units)
• Referral, annual, and spot bonuses
WORK ENVIRONMENT
This role is based in McLean, VA or Columbia, MD. We operate in a hybrid environment with flexibility for remote work, though on-site presence is expected 2–3 days per week for team collaboration.
Schedule: Monday – Friday. The ML team occasionally runs extended training jobs that may require monitoring outside standard hours.
We’re hiring Machine Learning Engineers to join our growing AI/ML team. You’ll work across the full ML lifecycle — from dataset preparation and model training to production inference and evaluation — for some of the most demanding customer mission sets in the federal space. This role is hands-on and production oriented. You’ll train generative models, build evaluation pipelines, optimize inference performance, and collaborate with product engineers to ship capabilities that matter. We’re looking for engineers who are comfortable working with PyTorch, large-scale GPU training, and who stay current on the rapidly evolving generative AI landscape.
WHAT YOU’LL DO
• Train, fine-tune, and evaluate generative AI models (diffusion models, GANs, LoRA adapters) for photorealistic image and video generation
• Build and maintain training pipelines: dataset curation, preprocessing, augmentation, hyperparameter tuning, and quality benchmarking across GPU clusters
• Develop evaluation frameworks to benchmark model outputs against commercial detection systems, quantifying pass rates and identifying weaknesses
• Optimize inference pipelines for throughput and latency, including model quantization, batching strategies, and GPU memory management
• Integrate open-source models (Flux family, Hugging Face Transformers, ComfyUI) into workflows with appropriate quality controls
• Collaborate with product engineering to deploy models behind production APIs (Django backend, Celery/Dramatiq task queues)
• Contribute to research on adversarial ML techniques, counter-detection methods, and emerging generative architectures
• Document model performance, training procedures, and architectural decisions for team knowledge sharing
REQUIRED QUALIFICATIONS
• 3+ years of professional experience in machine learning engineering, with hands-on generative model experience (diffusion models, GANs, or transformer-based generation)
• Strong proficiency in Python and PyTorch, with experience training models on multi-GPU setups
• Experience with at least one of: LoRA/QLoRA fine-tuning, Stable Diffusion / Flux model family, ComfyUI workflow development, or custom training pipelines
• Understanding of ML fundamentals: loss functions, optimization, regularization, evaluation metrics, and common failure modes
• Experience with data pipeline tools and practices: dataset versioning, preprocessing, and quality validation
• Familiarity with Linux environments, Docker, and cloud compute (AWS preferred)
• Strong debugging and experimentation skills — comfortable iterating on model architectures and training configurations
• Bachelor’s degree in Computer Science, Machine Learning, Mathematics, or a related field (or equivalent professional experience)
• U.S. citizenship (required for work with federal customers)
PREFERRED QUALIFICATIONS
• Experience with distributed training frameworks (Ray, DeepSpeed, PyTorch DDP/FSDP)
• Background in computer vision, image processing, or facial recognition systems
• Experience deploying ML models to production (model serving, API integration, monitoring)
• Familiarity with adversarial machine learning or AI safety research
• Experience with LLM fine-tuning, RAG systems, or agentic AI frameworks (LangChain/LangGraph)
• Publication record in ML/AI venues (NeurIPS, ICML, CVPR, etc.) or meaningful open-source contributions
COMPENSATION & BENEFITS
Comprehensive benefits package designed for high-performing professionals:
• Fully paid medical, dental, and vision insurance premiums
• 10% company contribution to a Vanguard-sponsored 401(k) plan, vested immediately
• 7 weeks paid time off, accrued semi-monthly — use it however you like
• Life, short-term disability, and long-term disability insurance
• Dependent care and health FSA accounts
• $5,000 annual professional development and tuition reimbursement
• $200 monthly communication allowance (phone and internet)
• $2,000 technology allowance every two years
• Employee Equity Plan (Woven Bonus Units)
• Referral, annual, and spot bonuses
WORK ENVIRONMENT
This role is based in McLean, VA or Columbia, MD. We operate in a hybrid environment with flexibility for remote work, though on-site presence is expected 2–3 days per week for team collaboration.
Schedule: Monday – Friday. The ML team occasionally runs extended training jobs that may require monitoring outside standard hours.
group id: 91123694