Senior Machine Learning Engineer at NeuReality

, , Israel -

Full Time

Start Date

Immediate

Expiry Date

25 Jul, 26

Salary

0.0

Posted On

26 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning Engineering, LLM Inference, Performance Optimization, System Integration, End-to-end Workflows, vLLM, TensorRT, SGLang, GPU, Kubernetes, Cloud Environments, Token Generation Pipelines, System Orchestration, Multi-tenant Management, Caching Strategies

Industry

Semiconductor Manufacturing

Description

NeuReality is looking for a Senior Machine Learning Engineer to work on LLM-based systems with a strong emphasis on integration, end-to-end workflows, and production readiness. This role combines performance optimization (prefill/decode, throughput, latency) with hands-on work integrating components across the AI platform. A significant part of the role involves building end-to-end integration flows and tests, particularly around token generation pipelines and system orchestration, as well as contributing to intelligent system behavior such as hardware selection and execution strategies. The role also includes developing system-level logic in Python for multi-tenant management, caching strategies, and service lifecycle management across the platform. Responsibilities: Build and maintain end-to-end integration flows across the AI inference pipeline (serving, orchestration, APIs, and infrastructure) Design, implement, and optimize LLM inference workflows, including prefill and decode stages Improve system performance with focus on throughput, latency, and interactivity Write production-grade components in Python and integrate them into the broader system Contribute to system-level logic such as smart hardware selection and execution strategies Integrate models (open source and custom), services, and APIs into cohesive, reliable end-to-end application pipelines Requirements 4+ years of experience in software engineering or machine learning engineering Strong proficiency in Python Strong experience with LLM inference systems and performance optimization Hands-on experience with system integration and end-to-end workflows Experience with inference frameworks such as vLLM, TensorRT, SGLang etc Experience working with GPU/accelerator-based systems Preferred Qualifications Hands-on experience with Dynamo and LLM-D for LLM inference and serving Familiarity with Kubernetes and cloud environments

Responsibilities

The role involves building and maintaining end-to-end integration flows for AI inference pipelines, including serving, orchestration, and infrastructure. You will design and optimize LLM inference workflows while developing system-level logic for hardware selection and multi-tenant management.