דרושים » תוכנה » Senior Back-End Engineer

Were looking for a skilled and curious Back End Developer to join our growing engineering team. In this role, you'll architect and build reliable, scalable services that power mission-critical fintech products - from payments and lending to compliance and blockchain.
You'll collaborate closely with product, DevOps, and front-end engineers to design robust APIs, implement clean data flows, and solve complex performance challenges.
Your work will shape how money moves securely across our infrastructure and directly support millions of users. This is a great opportunity to drive meaningful impact while learning and growing within a highly supportive and technical team.
What Youll Actually Be Doing:
Design, develop, and deliver scalable software modules and components
Build backend systems using .NET, DotNet 6.0, SQL Server, RabbitMQ, Docker and AWS
Plan, triage, and prioritize work across multiple priorities
Share your knowledge with teammates and help mentor and guide partners

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 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

We are looking for a backend engineer who can design, build, and operate highly reliable Node.js services on AWS that enable generative‑AI capabilities across our products and internal workflows.
You will create scalable APIs, data pipelines, and serverless architectures that integrate large‑language‑model (LLM) services such as Amazon Bedrock, OpenAI, and open‑source models, enabling teams to safely and efficiently leverage generative AI.
Who You Are:
You have experience building Retrieval‑Augmented Generation (RAG) systems or knowledge‑base chatbots.
You're Hands‑on with vector databases such as Pinecone, Chroma, or pgvector on Postgres/Aurora.
Have AWS certification (Developer, Solutions Architect, or Machine Learning Specialty).
Experience with observability tooling (Datadog, New Relic) and cost‑optimization strategies for AI workloads.
Background in microservices, domain‑driven design, or event‑sourcing patterns.
What Youll Actually Be Doing
Design and implement REST/GraphQL APIs in Node.js/TypeScript to serve generative‑AI features such as chat, summarization, and content generation.
Build and maintain AWS‑native architectures using Lambda, API Gateway, ECS/Fargate, DynamoDB, S3, and Step Functions.
Integrate and orchestrate LLM services (Amazon Bedrock, OpenAI, self‑hosted models) and vector databases (Amazon Aurora pgvector, Pinecone, Chroma) to power Retrieval‑Augmented Generation (RAG) pipelines.
Create secure, observable, and cost‑efficient infrastructure as code (CDK/Terraform) and automate CI/CD with GitHub Actions or AWS CodePipeline.
Implement monitoring, tracing, and logging (CloudWatch, X‑Ray, OpenTelemetry) to track latency, cost, and output quality of AI endpoints.
Collaborate with ML engineers, product managers, and front‑end teams in agile sprints; participate in design reviews and knowledge‑sharing sessions.
Establish best practices for prompt engineering, model evaluation, and data governance to ensure responsible AI usage.

Were looking for a Microsoft Dynamics 365 CRM Administrator to lead the implementation and evolution of our Microsoft ecosystem, ensuring smooth operations, scalability, and top-tier user experience across departments. In this critical role, youll design and administer our Dynamics 365 CRM and Power Platform systems while supporting high-impact business processes, from partner onboarding to enterprise workflow automation.
Youll work closely with stakeholders across time zones and business lines to translate needs into scalable configurations and sustainable architecture. This role offers a high degree of autonomy and influence within a mission-driven, fast-paced fintech environment

Were looking for a Senior Microsoft Dynamics 365 Configurator / Developer to take the lead in building seamless, scalable internal systems that power our operations. This role is critical to deliver world-class experiences for both our teams and end users by configuring and integrating Microsoft Dynamics 365 CRM and the full Power Platform suite. Youll have significant autonomy, working across Power Pages, Power Automate, Teams, and SharePoint to design and implement best-in-class solutions. Your work will directly support regulatory compliance, partner success, and internal workflows, reducing tech debt and improving user experience.
As part of a remote-friendly team working mostly NY hours, youll collaborate with peers across time zones while growing your technical leadership.

As a Large Scale Training Engineer, you will play a key role in enhancing the training throughput of our internal framework and enabling researchers to pioneer new model concepts. This role demands excellent engineering skills for designing, implementing, and optimizing cutting-edge AI models, alongside writing robust machine learning code and understanding supercomputer performance deeply. Your expertise in performance optimization, understanding distributed systems, and bug elimination will be crucial, as our framework supports extensive computations across numerous virtual machines.

This role is designed for individuals who are not only technically proficient but also deeply passionate about pushing the boundaries of AI and machine learning through innovative engineering and collaborative research.

Key Responsibilities
Profile and optimize the training process to ensure efficiency and effectiveness, including optimizing multimodal data pipelines and data storage methods.
Develop high-performance TPU/GPU/CPU kernels and integrate advanced techniques into our training framework to maximize hardware efficiency.
Utilize knowledge of hardware features to make aggressive optimizations and advise on hardware/software co-designs.
Collaboratively develop model architectures with researchers that facilitate efficient training and inference.
Design, maintain, and evolve a high-quality, shared codebase that emphasizes correctness, readability, extensibility, testing, and long-term maintainability, while balancing performance requirements.

This role focuses on pioneering model architecture and pre-training algorithms, shaping the next generation of our foundational generative AI models.

What you will be doing
Pre-train and fine-tune video, audio, and image generative models to pursue state-of-the-art results.
Publish papers and open source models to benefit the research community and advance the field.
Design and implement machine learning models for text-to-audio and text-to-video generation.
Collaborate with data engineers to curate and preprocess text and video data.
Optimize models for high performance, ensuring efficient training and inference.
Build new controls and capabilities into generative text-to-audio and text-to-video models.
Stay updated with the latest developments in Generative AI, particularly in the fields of image, video, and audio.
Work closely with product teams to integrate AI models into applications and services.
Conduct experiments and prototype new concepts to advance the capabilities of our AI tools.

We are seeking a researcher to join the LTX Model Applications team, focusing on advancing and extending the capabilities of our LTX-Video models. You will design and explore new use cases, train novel ways to control the generation process, and push the boundaries of generative video technology. Your work will directly influence millions of users through our apps and contribute to the broader research and open-source communities, shaping the next generation of creative tools.

What you will be doing
Advance and extend the capabilities of our LTX-Video models through new controls, features, and use cases.
Design and implement machine learning models for video, image, and audio generation, as well as multimodal applications.
Conduct experiments, prototype concepts, and validate ideas to expand generative video technology.
Collaborate with engineers, designers, and product managers to translate research into impactful product features.
Regularly open-source code, models, and research insights, contributing to the academic and open-source communities.
Stay at the forefront of generative AI research in video, image, and audio, and bring relevant advances into our products.
Write high-quality, efficient code and maintain best practices in research workflows.
Mentor other researchers and lead collaborative projects.