Senior Software Engineer / Research Engineer – Large‑Scale Statistical Systems (USCB Program)
Experience: 10+ years software engineering; 5+ years Python/R. Would consider # of years experience with PHD in a quantitative discipline.
CPMC is seeking a Senior Software Engineer who thrives on technically demanding problems involving large-scale computation, complex algorithms, and high‑performance data processing. You will engineer the U.S. Census Bureau’s Disclosure Avoidance System (DAS), a system that executes advanced statistical and differential privacy algorithms across massive datasets.
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Understanding how algorithms behave under realworld scale
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Turning research prototypes into robust, high‑performance systems
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Diagnosing subtle numerical, performance, or correctness issues
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Building distributed systems that must be reproducible, efficient, and scientifically trustworthy
You’ll be building the computational machinery that makes cutting‑edge statistical methods run reliably at national scale.
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Engineer productiongrade implementations of complex statistical and differential privacy algorithms, ensuring correctness, stability, and performance
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Translate research code (Python/R) into optimized, maintainable systems, often requiring algorithmic insight and careful handling of numerical edge cases
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Design and optimize largescale data processing pipelines for ingestion, transformation, validation, and output generation
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Profile, benchmark, and optimize distributed workloads (Spark, EMR, containerized compute) to reduce runtime and cost
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Diagnose algorithmic performance issues—from data skew to solver behavior to memory pressure
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Collaborate deeply with statisticians to understand algorithmic assumptions, constraints, and expected behavior under scale
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Develop reproducible experiment frameworks, including parameter tracking, environment isolation, and deterministic execution
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Build automation and tooling that enable researchers to run large experiments safely and efficiently
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Tune compute and solver configurations (Spark, Gurobi, storage layouts, partitioning strategies) for largescale statistical workloads
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Support distributed execution environments and contribute to DevOps/automation where needed to keep the system reliable
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10+ years professional software engineering experience
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Strong programming skills in Python (primary) and familiarity with R
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Experience with distributed computing (Spark, EMR, or equivalent)
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Strong background in performance engineering, profiling, and debugging complex systems
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Experience building and maintaining largescale data pipelines
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Handson experience with AWS (EMR, S3)
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Experience with CI/CD, automated testing, and environment management
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Familiarity with basic probability and statistics concepts (e.g. hypothesis testing, probability distributions, least squares, etc.)"
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Ability to read, reason about, and improve scientific or researchoriented code
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Experience collaborating with statisticians or working in scientific computing environments
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Familiarity with numerical methods, statistical computing, or algorithmic evaluation
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Experience with optimization solvers (e.g., Gurobi) or largescale simulations
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Knowledge of differential privacy or privacypreserving computation
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Experience with containerization (Docker, Kubernetes)
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Experience with HPC or large distributed systems
Why This Role Is Technically Unique
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You work on algorithmically complex systems where correctness and performance both matter
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You operate at nationalscale data volumes with strict reproducibility requirements
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You collaborate with researchers pushing the boundaries of statistical privacy
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You solve problems where the bottleneck might be a numerical instability, a distributed shuffle, a solver configuration, a data partitioning strategy, or an algorithmic assumption that breaks at scale
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You directly influence the performance and reliability of a system that protects the confidentiality of Census data
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Organizational Skills: Can plan and prioritize work. Follows tasks to their logical conclusion and makes sure that everything has been done to the right standard. Good attention to detail.
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Team Work: Able to enthuse and maintain project interest. Comfortable working both individually and as part of a team. Prepared to challenge ideas within a group in a constructive way.
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Communications: Ability to communicate clearly and efficiently to team members and clients, verbally and in writing. Able to present ideas in a variety of ways depending upon audience and context. Excellent active listening skills.
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Problem Solving: Natural inclination for planning strategy and tactics. Ability to analyze problems and determine root cause, generating alternatives, evaluating and selecting alternatives and implementing solutions.
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Results oriented: Able to drive things forward regardless of personal interest in the task.
