SimSpace serves as an AI Proving Ground where organizations can confidently train, test, and outmaneuver adversaries in any environment. Trusted by allied governments, militaries, enterprises, and research institutions worldwide, SimSpace enables adaptive, AI-ready defenses that stay ahead of evolving threats. Founded in 2015 by experts from U.S. Cyber Command and MIT Lincoln Laboratory, the platform unifies training, testing, and validation in a realistic, live-fire simulation—helping teams evaluate security investments, optimize performance, and compress cyber readiness cycles from months to days.
Why join SimSpace? We are an organization that is focused on building our culture and mindfully enhancing our atmosphere every day which is why we have collaborated on an integral value system. Our governing philosophy of being Human Centered is deeply embedded within our value system. We apply this philosophy to every one of our internal team members, external clients, and their customers.
How Do We Work? We believe that people are at the center of everything we do. SimSpace fosters a culture of continuous learning, curiosity, and professional growth. That belief shows up in action: in-house training, internal and external learning platforms, cyber conferences, industry events, and dedicated time for skill development. Our people are empowered to shape their careers - and it shows. Year over year, SimSpace consistently outperforms industry benchmarks in internal mobility, promotions, and total rewards growth.
Who Thrives Here? We are a team of innovators, protectors, and problem-solvers. We believe diversity of thought and experience fuels better solutions, and we’re committed to building teams that reflect the communities we serve. Whether you’re remote or office-based, you’ll collaborate with talented colleagues across departments and time zones, united by the mission to create a safer digital world.
We invite you to apply today!
Staff Data Science Engineer sought by SimSpace Corporation (Boston, MA).
JOB DUTIES
Design, implement, and deploy advanced mathematical and machine-learning algorithms (e.g., supervised, unsupervised, reinforcement learning, NLP, anomaly detection) to support cyber-range simulations, delivering production models with documented accuracy, latency, and throughput metrics.
Develop and maintain end-to-end AI/ML pipelines (data ingestion, feature engineering, model training, validation, inference, monitoring), ensuring test coverage, reproducibility of experiments, and documented performance benchmarks.
Construct and optimize numerical methods and computational models using Python, NumPy, SciPy, Pandas, and JAX/TensorFlow/PyTorch to solve large-scale (10M+ row) data and optimization problems relevant to cyber-range operations.
Architect scalable model-serving systems in Docker/Podman/Kubernetes, achieving reliable deployments with measured service uptime of 99 percent or greater and documented resource-utilization improvements.
Develop and integrate new AI-driven cybersecurity capabilities (e.g., automated scoring engines, classification systems, reinforcement-learning-based adversary behaviors) with quantified gains in accuracy, precision/recall, or scenario realism, validated against internal evaluation datasets.
Author and maintain production-quality Python services, enforcing code standards, implementing unit/integration testing with unittest/pytest, and reducing defect rates via measurable static/dynamic analysis reports.
Design, evaluate, and improve model performance using quantitative metrics (e.g., AUC, F1, perplexity, reward curves, convergence rates), generating written model-evaluation reports used in release readiness decisions.
Perform algorithmic research on emerging ML/AI/cyber methods, producing technical assessments, prototypes, and feasibility studies that directly inform quarterly engineering and product roadmaps.
Lead cross-team technical initiatives, producing written design documents, conducting architecture reviews, and driving the integration of DS/AI services across engineering, product management, platform teams, and cybersecurity content engineering.
Mentor senior-level engineers and data scientists by conducting formal code reviews, mathematical model reviews, and algorithm correctness checks, with documented feedback that improves model accuracy, stability, or performance.
Apply computational mathematics methods (e.g., linear algebra, numerical optimization, differential equations, stochastic processes) to design, implement, and validate algorithms and models with documented quantitative results.
Produce internal documentation (design specs, API references, model cards, validation reports) ensuring compliance with internal engineering, security, and AI governance standards.
Define and establish technical standards, best practices, and design patterns for AI/ML development across the Data Science team.
Drive high-performance computing initiatives to optimize AI/ML system performance, including distributed computing and GPU acceleration strategies.
Collaborate with cross-functional teams, including product development, engineering, cybersecurity content developers, and external stakeholders to align technical solutions with organizational objectives.
Prepare and deliver technical reports, presentations, and briefings to leadership, stakeholders, and customers on project status, technical approaches, and strategic recommendations.
Evaluate and recommend new technologies, tools, and methodologies to advance SimSpace's AI/ML and cybersecurity capabilities.
Attend and participate in team and company meetings as well as contribute to strategic planning and technical roadmap development.
May work remotely from anywhere in the US.
MINIMUM REQUIREMENTS
Ph.D. in Computational Mathematics, Computer Science, Applied Mathematics, or a closely related field.
1 year of experience in computational mathematics, scientific computing, machine learning, data science, or algorithm development. Experience may be gained through employment, research, or doctoral work.
Demonstrated experience applying mathematical or machine-learning algorithms (e.g., regression, classification, clustering, reinforcement learning, NLP, numerical optimization) to datasets of at least 1 million observations or high-dimensional data.
Demonstrated experience developing scientific or ML software in Python using at least three of the following packages: NumPy, Pandas, SciPy, Matplotlib.
Demonstrated experience implementing, training, and validating machine-learning models using at least three of the following frameworks: PyTorch, TensorFlow, JAX, scikit-learn.
Demonstrated experience writing automated tests for ML or scientific code using at least two of the following: unittest, pytest, hypothesis.
Demonstrated experience building and deploying containerized applications using at least one of the following: Docker, Podman, Kubernetes.
Demonstrated experience producing documented research or production-quality software artifacts (e.g., peer-reviewed publications, open-source contributions, internal enterprise algorithms or models) demonstrating algorithm correctness or performance validation.
Demonstrated experience applying computational mathematics methods (e.g., linear algebra, numerical optimization, differential equations, stochastic processes, network or graph analysis) to design or evaluate algorithms or models, with documented quantitative results.
Demonstrated understanding of statistics, computational complexity and performance, parallelization, databases, optimization, linear programming, hypothesis testing, research methodology, and existing scientific literature and results in the field of data science and AI/ML.
*Experience may be gained through academic coursework during or after master’s or PhD degree.
*Experience may be gained concurrently
**Demonstrated knowledge or experience is equivalent to at least 6 months of experience as it cannot be learned during a reasonable period of on-the-job training.
**May work remotely from anywhere in the US
simspace-corporation