ITG

About the Role

We’re looking for a GPU Performance Engineer to optimize and scale high-performance AI workloads at the kernel level.

This role focuses on deep performance engineering — working directly with CUDA kernels, GPU architecture, and profiling tools to unlock efficiency across training and inference systems. You’ll work on compute-intensive workloads where every microsecond and memory access pattern matters.

If you enjoy low-level optimization, understanding how hardware really works, and pushing systems to their limits — this role is for you.

What You’ll Do

• Design, optimize, and debug CUDA kernels for high-performance AI workloads
• Improve performance of compute-intensive operations (e.g., GEMM, attention, MoE, graph workloads)
• Apply low-level optimization techniques across memory hierarchy, warp execution, tensor cores, and compute efficiency
• Profile and eliminate GPU bottlenecks using tools like Nsight Systems and Nsight Compute
• Integrate optimized kernels into modern AI training and inference stacks
• Build internal tooling for benchmarking, profiling, and performance analysis
• Collaborate with engineering teams on performance-critical model infrastructure
• Contribute reusable performance improvements and best practices across the stack

What We’re Looking For

• Strong hands-on experience with CUDA kernel development and optimization
• Deep understanding of GPU architecture (memory hierarchy, warp scheduling, synchronization)
• Strong C/C++ skills for high-performance systems programming
• Proven experience identifying and resolving GPU performance bottlenecks
• Experience optimizing compute-heavy workloads in real-world systems
• Familiarity with GPU profiling tools (Nsight Systems, Nsight Compute, or similar)
• Experience integrating custom kernels into ML frameworks
• Strong debugging skills and performance-oriented mindset

Nice to Have

• Experience with PTX and architecture-specific optimizations
• Familiarity with modern GPU architectures (e.g., Hopper, Blackwell)
• Experience optimizing Transformer workloads (e.g., attention, FlashAttention)
• Familiarity with Triton, CUTLASS, Thrust, or CUB
• Experience with multi-GPU systems (e.g., NVLink, NCCL)
• Open-source contributions or relevant research in GPU / HPC

Why This Role

• Work on cutting-edge AI systems with real performance impact
• Solve complex low-level engineering problems at the hardware boundary
• High ownership over performance-critical components
• Opportunity to push modern GPU systems to their limits

We’re looking for a Platform Engineer to help build and scale secure, reliable, and production-ready infrastructure.

This role focuses on designing and operating Kubernetes-based environments, infrastructure as code, and internal platform systems that support engineering teams. You’ll work on real production workloads where reliability, scalability, and operational excellence matter.

If you enjoy building robust infrastructure, improving system reliability, and enabling teams through better platform tooling — this role is for you.

What You’ll Do

• Design, build, and operate scalable infrastructure and platform systems
• Manage and optimize Kubernetes-based environments
• Develop infrastructure as code using tools like Terraform
• Build and maintain CI/CD pipelines and deployment workflows
• Improve observability, monitoring, and system reliability
• Support secure and resilient environments, including restricted or isolated deployments when needed
• Collaborate with engineering teams on platform architecture and production workflows
• Troubleshoot infrastructure issues and improve performance, scalability, and maintainability
• Contribute to internal tooling and engineering enablement

What We’re Looking For

• 3+ years of experience in Platform Engineering, DevOps, SRE, or infrastructure roles
• Strong hands-on experience with Kubernetes and Terraform
• Experience building and maintaining CI/CD pipelines
• Solid understanding of observability and monitoring systems
• Experience with infrastructure as code and containerized environments
• Familiarity with Docker and Kubernetes-native architectures
• Proficiency in at least one programming or scripting language (Go, Python, or similar)
• Strong understanding of production systems, reliability, and security best practices

Nice to Have

• Experience with secure, air-gapped, or isolated deployment environments
• Familiarity with tools like GitLab CI, Prometheus, Grafana, or Keycloak
• Experience with backend systems (e.g., gRPC services)
• Exposure to ML infrastructure or distributed systems
• Strong backend engineering background

Why This Role

• Work on production-grade infrastructure and real-world systems
• High ownership over platform architecture and reliability
• Opportunity to build scalable systems used by engineering teams
• Collaborative and fast-paced environment

We’re looking for a Computer Vision Engineer focused on evaluation and data quality to help improve the performance of real-world CV systems.

This role is centered around working with visual datasets, analyzing model outputs, and identifying failure patterns. You won’t be training models end-to-end — instead, you’ll play a key role in improving model quality through better data, validation, and structured analysis.

If you enjoy working hands-on with images and video data, spotting edge cases, and making systems more accurate and reliable — this role is for you.

What You’ll Do

• Review, annotate, and validate image and video datasets used in CV workflows
• Perform detailed error analysis on model outputs to identify weaknesses and edge cases
• Support evaluation workflows for tasks such as object detection, classification, segmentation, and pose estimation
• Improve data quality through structured labeling, annotation review, and consistency checks
• Help define evaluation criteria, benchmarks, and test datasets
• Collaborate with ML and CV engineers to provide clear feedback on model behavior
• Document findings, patterns, and recommendations
• Contribute to improving data pipelines and evaluation processes

What We’re Looking For

• 2+ years of experience in Machine Learning or Computer Vision
• Strong understanding of CV fundamentals and evaluation metrics
• Hands-on experience with image/video datasets, annotation, or CV model evaluation
• Familiarity with tasks such as object detection, classification, segmentation, or pose estimation
• Ability to analyze visual outputs and perform structured error analysis
• Strong Python skills
• Experience with tools or frameworks such as OpenCV, PyTorch, or TensorFlow
• High attention to detail and comfort with quality-focused work

Nice to Have

• Experience with multimodal or vision-language models
• Familiarity with tools like Labelbox, Scale AI, CVAT, or Roboflow
• Background in data-centric AI, visual QA, or dataset auditing
• Experience with 3D vision, depth estimation, or point cloud data
• Kaggle or CV competition experience

Why This Role

• Work on real-world computer vision systems and improve their accuracy
• Direct impact on model performance through data and evaluation
• High ownership over dataset quality and evaluation processes
• Collaborative and fast-moving engineering environment

About the Role

We’re looking for a Machine Learning Engineer focused on evaluation and data quality to help improve the performance of modern ML and LLM systems.

This role is centered around understanding model behavior, identifying failure patterns, and building strong evaluation and feedback loops. You won’t be training models directly — instead, you’ll play a critical role in improving model outcomes through better data, analysis, and experimentation.

If you enjoy analytical problem-solving, working with real-world data, and making ML systems measurably better — this role is for you.

What You’ll Do

• Analyze and improve datasets used in ML and LLM workflows
• Perform detailed error analysis on model outputs (qualitative and quantitative)
• Design and implement evaluation frameworks, benchmarks, and quality checks
• Identify failure modes, data gaps, and improvement opportunities
• Support model experimentation with structured insights and feedback
• Collaborate closely with ML engineers and researchers to improve system performance
• Document evaluation methodologies, findings, and recommendations
• Help maintain high standards for data quality, consistency, and bias awareness

What We’re Looking For

• 5+ years of experience in Machine Learning or Applied ML roles
• Strong understanding of ML fundamentals and evaluation methodologies
• Hands-on experience working with real-world datasets for ML or LLM systems
• Strong Python skills
• Experience with data validation, evaluation pipelines, and error analysis
• Strong analytical thinking and attention to detail
• Ability to work independently and clearly communicate insights

Nice to Have

• Experience with LLMs, NLP, or generative AI systems
• Background in data annotation, QA, or evaluation-heavy ML environments
• Experience with experiment tracking, prompt evaluation, or benchmark design
• Exposure to bias analysis, robustness testing, or dataset auditing
• Research or competition-based ML background

Why This Role

• Work on cutting-edge ML and LLM systems
• Direct impact on model quality and real-world performance
• High ownership in shaping evaluation and data practices
• Collaborative, fast-moving engineering environment

About the Role

We’re looking for a strong Backend Engineer to help build and scale the systems that power complex AI training and execution workflows.

This is a high-impact role focused on designing and operating distributed systems that process large volumes of data and tasks. You’ll work on core backend infrastructure where performance, reliability, and scalability are critical.

If you enjoy building production systems from the ground up, solving complex engineering problems, and owning systems end-to-end — this role is for you.

What You’ll Do

• Design and build backend services that support large-scale AI workflows and task execution
• Develop and operate high-throughput, low-latency systems
• Build distributed workflows and orchestration components
• Collaborate closely with ML engineers, researchers, and product teams
• Own production systems: reliability, performance, monitoring, and scalability
• Contribute to system architecture and engineering best practices

What We’re Looking For

• 3+ years of experience building and shipping backend systems in production
• Strong fundamentals in system design, data structures, and distributed systems
• Experience building systems that operate at scale
• Proficiency in Python or Go
• Solid understanding of databases, async processing, and backend architecture
• Ability to work in fast-moving, high-ownership environments
• Strong problem-solving skills and engineering judgment

Nice to Have

• Experience with workflow engines or orchestration systems
• Background in infrastructure-heavy or AI-adjacent systems
• Experience building early-stage (0→1) products or platforms
• Computer Science degree or equivalent practical experience

Why This Role

• Work on technically challenging systems at scale
• High ownership and real impact on product and architecture
• Opportunity to shape core infrastructure from early stages
• Collaborative environment with strong engineering culture