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Your role is central to driving quality, scalability, and accountability in processes such as compliance audits, credit bureau furnishing, and legal automation. This is an exciting opportunity to work at the intersection of technology, compliance, and data. You will lead Backend Engineers and Data Engineers to build robust systems that power regulatory functions. You will have the chance to shape the technical strategy and build the team (hiring and mentoring), while remaining hands-on with modern technologies.

Key Responsibilities:

Manage and mentor a dynamic team of 3 engineers (Backend & Data), fostering collaboration and professional growth. You will also be responsible for hiring and onboarding new talent to the team.

Architect scalable, high-performance systems that support compliance-critical operations. You will define the technical roadmap and drive modernization efforts.

Design and oversee both Backend infrastructure and Data Pipelines to ensure accurate, auditable, and scalable compliance processes.

Act as the single point of contact for legal and compliance stakeholders, handling and facilitating all technical aspects of audit requests (SOC, state, and other regulatory audits).

Collaborate with cross-functional teams, including other engineering teams, product managers, legal, and business stakeholders, to align technology and compliance goals.

Remain active in the codebase, ensuring high code quality and leading by example.

As an ML Software Engineer with a focus on low-level and CUDA-based optimizations, you will play a key role in shaping the design, performance, and scalability of machine learning inference systems. Youll work on deeply technical challenges at the intersection of GPU acceleration, systems architecture, and ML deployment.
Your expertise in CUDA, C/C++, and performance tuning will be crucial in enhancing runtime efficiency across heterogeneous computing environments. Youll collaborate with designers, researchers, and backend engineers to build production-grade ML pipelines that are optimized for latency, throughput, and memory use, contributing directly to the infrastructure powering next-generation AI products.This role is ideal for an engineer with strong systems-level thinking, deep familiarity with GPU internals, and a passion for pushing the boundaries of performance and efficiency in machine learning infrastructure.

Responsibilities
Design and implement highly optimized GPU-accelerated ML inference systems using CUDA and low-level parallelism techniques
Optimize memory, compute, and data flow to meet real-time or high-throughput constraints
Improve the performance, reliability, and observability of our inference backend across diverse compute targets (CPU/GPU)
Collaborate with cross-functional teams (including researchers, developers, and designers) to deliver efficient and scalable inference solutions
Contribute to ComfyUI and internal infrastructure to improve the usability and performance of model execution flows
Investigate performance bottlenecks at all levels of the stack-from Python to kernel-level execution
Navigate and enhance a large, complex, production-grade codebase
Drive innovation in low-level system design to support future ML workloads

As a Research Scientist in Model Evaluation, you are the ultimate authority on model quality and utility. You will design the automated judges, reward models, evaluation datasets, and benchmarking ecosystems that determine the future of LTX. Your mission is to provide the "ground truth" for our pre-training and post-training teams. You will blend the rigor of a researcher with the intuition of a product-thinker, developing metrics that capture both the aesthetic soul of a video and the functional precision required for high-stakes professional use.

Key Responsibilities
Steer Training & Research: Systematically evaluate model checkpoints to provide actionable insights that guide training experiments and architectural decisions.
Design Benchmark Ecosystems: Develop and run rigorous benchmarks for release candidates against competitive models, ensuring LTX-2 remains world-class.
Build Next-Gen Metrics: Develop robust automatic metrics and Reward Models (e.g., for RL, ITS, auto-research agents) that quantify complex attributes like temporal coherence, physical correctness, spatial accuracy, and foley synchronization.
Diagnose & Analyze: Perform deep root-cause analysis on model failures, providing the diagnostic clarity needed for researchers to implement targeted fixes.
Scale Evaluation: Collaborate with platform engineers to deploy evaluation frameworks across large-scale GPU clusters.