Lead research at the intersection of industry-scale data and academic rigor in Document Understanding, designing AI-native agentic systems to unlock insights from billions of historical and genealogical records.
Responsibilities
Innovate with State-of-the-Art AI
Architect Agentic Systems
Analyze and Optimize Multi-Modal Models
Natural Language Processing
Computer Vision
Requirements
Ph.D. in Computer Science, Data Science, Statistics, Linguistics, Engineering, or a related quantitative field
Nice to have: Strong record of academic publications in NLP, CV, or Agentic AI
Nice to have: Research in inference efficiency and optimization
Nice to have: Experience with cloud platforms and AI/ML services
Nice to have: Ability to present complex technical solutions to stakeholders
Responsible for algorithm modeling, analysis, and implementation for ZEISS products, encompassing both 2D and 3D imaging solutions, as well as related imaging technologies.
Responsibilities
CT/Xray image algorithm modeling, analysis, and implementation
Core technical issue solving and optimization
Cross-site technical alignment
Work with team to build and drive the tech stack development
Develop and optimize algorithms for image processing, machine learning, and model deployment
Adapt AI models across platforms to enable performance and accuracy measurement on Linux and Windows hosts, x86 and Arm environments, mobile and target devices, MTPs, instruction-set simulators (ISS), and supported Hexagon architectures.
Design, implement, and maintain system-integration, regression, preflight, and release-validation workflows.
Analyze failures, crashes, output mismatches, accuracy changes, and performance regressions.
Integrate test workflows with Git, GitHub or Gerrit, CI systems, scheduled jobs, service accounts, target stations, and automated result distribution.
Improve test coverage, reproducibility, execution time, environment stability, and visibility into regression health and release readiness.
Requirements
Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, or a related field
Nice to have: Experience testing embedded, DSP, accelerator, or heterogeneous-compute systems
Nice to have: Experience with C or C++
Nice to have: Experience with pytest
Nice to have: Experience performing cross-platform validation
Apply AI engineering expertise to deploy, optimize, and scale models for strategic customers, leading technical journeys from POCs to production endpoints.
Responsibilities
Scope workloads, define metrics (TTFT, throughput, cost/token), build benchmarks, and lead technical POCs.
Serve as the technical lead for customer engineering teams through architecture reviews and rollout planning.
Tune engines (KV cache, speculative decoding, parallelism, quantization) to hit aggressive performance targets.
Profile and diagnose bottlenecks at the engine level to deliver production fixes.
Own end-to-end customer onboarding, deployment, autoscaling, capacity planning, and SLO monitoring.
Lead LLM inference optimization for Qualcomm Cloud AI accelerators, working with customers and internal teams to deploy and tune generative AI workloads.
Responsibilities
Deploy and optimize LLM and multimodal inference workloads on Qualcomm Cloud AI accelerator platforms
Profile and optimize prefill and decode performance metrics
Analyze model architecture and runtime bottlenecks across compute, accelerator memory, host memory, KV cache, interconnect, networking, and software stack
Optimize model execution using quantization, batching, continuous/dynamic batching, KV-cache management, paged attention, prompt/context-length tuning, and speculative decoding
Optimize distributed inference across tensor, pipeline, data, context, and expert parallelism
Requirements
Master’s degree or higher in Computer Science, Electrical/Computer Engineering, AI/ML, or related technical field
Lead on-device AI inference and systems for Unity’s browser-native runtime, driving performance, optimization, and integration across mobile, desktop, and web platforms.
Compensation
Base Salary Range: Zone A: $250,300 - $325,400
Responsibilities
Own end-to-end optimization pipeline for on-device AI models
Make decisions on quantization, pruning, and distillation
Write and tune WebGPU compute shaders and native kernels
Profile and eliminate bottlenecks with browser and platform tools
Design integration between ML runtime and game engine
The Computer Vision Algorithm Engineer role focused on ADAS perception turns camera video feeds (image frames) into a clear understanding of the vehicle’s surroundings. The work spans concept through serial production: design and simulate algorithms, analyze and replay data, build and test classical and deep learning models, and optimize for real-time execution on production ECUs.
Responsibilities
Develop (design, implement, optimize) conventional image processing algorithms for automotive embedded serial production projects.
Design, develop/tune, and optimize deep learning models for ADAS computer vision features (e.g., pruning, quantization) and improve computational performance.
Plan and execute experiments to assess deep learning model effectiveness, compare architectures, and validate results through rigorous component/bench testing.
Strong knowledge of various camera models; lens distortion correction; homograph and projective transformations mathematical techniques
Analyze large datasets to extract insights, refine models, and improve overall performance and robustness.
System Integration Engineer, SeniorQualcomm · Hod HaSharon, Israel
Staff Applied AI EngineerCrusoe · Tel Aviv, Israel