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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building production ML systems and training infrastructure, with a strong focus on Python programming and experience in optimizing data workflows and pipelines. Capable of collaborating with cross-functional teams to enhance model deployment and MLOps practices.
Highest-signal resume keywords
Python ProgrammingML Training PipelinesPyTorchTensorFlowMLOps Practices
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software EngineeringData WorkflowsModel Integration SystemsInfrastructure DevelopmentData CollectionData CurationPreprocessingCode ReviewProduction ReadinessInference Optimization
Soft Skills
CollaborationDocumentationProblem Solving
Tools & Technologies
C++C#JavaRustGo
Industry Keywords
Machine LearningResearch PlatformsModel Serving InfrastructureTechnical BlockersEngineering Standards
Tech Stack
Tools & technologiesGoJavaPythonPyTorchRustTensorflow
About the role
Key responsibilities & impact- Design and build training pipelines, data workflows, and model integration systems
- Develop infrastructure that accelerates research iteration and reduces turnaround time
- Build systems for data collection, curation, and preprocessing at scale
- Create tools and automation that move experiments toward production readiness
- Optimize data pipelines for reliability, performance, and observability
- Collaborate with ML researchers to understand their needs and remove technical blockers
- Work on model serving infrastructure and integration with the production framework
- Write clean, well-tested code that maintains high engineering standards
- Participate in code reviews and help raise the engineering bar across the team
- Contribute to shared tools, infrastructure, and cross-role projects (20% Time)
- Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction
- Document systems and decisions to support team knowledge sharing
Requirements
What you’ll need- 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms.
- Proficiency in Python, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go).
- Hands-on experience with PyTorch or TensorFlow in production or research environments.
- Experience building or maintaining ML training pipelines or data workflows.
- Familiarity with model deployment, inference optimization, or MLOps practices.
Benefits
Comp & perks- Competitive compensation package.
- Flexible working hours and vacation policy.
- Product-driven culture that treasures talents and individual growth.
- Front-row seat and hands-on experience with cutting edge technologies in the evolving gaming field
