$130.40K - $156.60K/yr
Full-time
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Job description
About The Job Alignerr connects top technical experts with leading AI labs to build, evaluate, and improve next-generation models. We work on real production systems and high-impact research workflows across data, tooling, and infrastructure. Position Senior C# Full-Stack Engineer — AI Data & Infrastructure Type: Contract, Remote Commitment: 20–40 hours/week Compensation: Competitive, hourly (based on experience) Role Responsibilities Design, build, and optimize high-performance systems in C# supporting AI data pipelines and evaluation workflows Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control Improve reliability, performance, and safety across existing C# codebases Collaborate with data, research, and engineering teams to support model training and evaluation workflows Identify bottlenecks and edge cases in data and system behavior, and implement scalable fixes Participate in synchronous reviews to iterate on system design and implementation decisions Qualifications Must-Have Native or fluent English speaker Full-stack developer experience with a strong systems programming background 5+ years of professional experience writing production C#.
Strong background in building streaming data pipelines using asynchronous streams and reactive programming concepts. Experience optimizing I/O-bound operations and implementing resilient retry policies for distributed data ingestion. Clear written and verbal communication skills.
Ability to commit 20–40 hours per week. Preferred Prior experience with data annotation, data quality, or evaluation systems Familiarity with AI/ML workflows, model training, or benchmarking pipelines Experience with distributed systems or developer tooling Application Process Submit your resume Complete a short technical screening Project matching and onboarding #J-18808-Ljbffr
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Frequently asked questions
Q: What skills or qualities help someone succeed as a Data Software Engineer?
A: To succeed as a Data Software Engineer, key technical skills include proficiency in programming languages such as Python, Java, or C++, as well as expertise in data structures, algorithms, and software development methodologies like Agile. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial, as Data Software Engineers often work with cross-functional teams and stakeholders to design, develop, and deploy data-driven solutions. By combining technical expertise with strong soft skills, Data Software Engineers can effectively drive business outcomes, innovate, and adapt to the rapidly evolving landscape of data technology.
Q: What is the career path for a Data Software Engineer?
A: A Data Software Engineer's typical career progression involves starting as a Junior Software Engineer, where they focus on developing and maintaining data-driven software applications, and gradually advancing to roles such as Senior Software Engineer, Technical Lead, or Data Architect, where they oversee large-scale data systems and lead cross-functional teams. Key opportunities for skill development include learning programming languages like Python, SQL, and Java, as well as data science tools like Hadoop, Spark, and machine learning frameworks like TensorFlow and PyTorch. Long-term, Data Software Engineers may pursue leadership roles, such as Director of Engineering or Chief Technology Officer, or transition into related fields like data science, product management, or entrepreneurship.
