דרושים » הנדסה » Applied AI Scientist - On Site

As an AI/ML Solution Engineer in the AI-Native Development team, you will design and build AI-powered development pipelines, evaluate ML approaches for code generation and review, and drive the adoption of AI-assisted software development across the organization. You will work at the intersection of machine learning and software engineering - selecting the right models, feedback strategies, and evaluation frameworks to make AI-generated code reliable, high-quality, and trustworthy.

What you'll be doing:

Drive architecture, applied research, and hands-on development by defining and building AI-native software engineering solutions.

Design and build AI-powered development pipelines - from code generation and automated review to feedback loops and evaluation systems.

Evaluate and select ML approaches for specific problems: when to use LLM prompting vs. fine-tuning (QLoRA), classical ML (random forest, linear regression) vs. reinforcement learning, RAG vs. structured extraction.

Architect feedback and evaluation systems that measure and improve AI output quality over time.

Review and refine AI solution architectures - evaluate design decisions, identify weaknesses, propose alternatives with reasoning.

Lead proof-of-concept development to validate new AI/ML approaches for development tooling.

Collaborate with the core team to define risk-based development levels and calibrate AI review depth per level.

a rapidly growing startup company, developing the next-generation smart-infrastructure solution based on novel fiber-sensing technology (smart roads, smart cities, perimeters, and grid monitoring, etc.). The company offers an award-winning disruptive solution; a sensor free approach to smart infrastructure. The company is VC backed and in the revenues stage. Combining pioneering technology in optical fiber sensing with state-of-the-art Machine Learning, we help prevent environmental disasters, protect human lives, and keep critical energy and transportation backbones running smoothly. We are looking for a talented, self-driven, and hands-on DSP Researcher to join our Photonics R&D team. In this role, you will research and develop next-generation distributed fiber-optic sensing technologies, focusing on signal processing, system modeling, simulations, and experimental validation. Working at the intersection of physics, electro-optics, fiber-optic sensing, and DSP, you will transform complex physical phenomena into robust sensing solutions for real-world applications. You will tackle challenging physical and algorithmic problems, contribute to cutting-edge sensing products, and collaborate closely with multidisciplinary R&D teams. This role is ideal for someone who thrives in a fast-paced environment, enjoys working across disciplines, and is excited to take ideas from research and lab validation to large-scale deployment.
 
What Youll Do:
* Conduct experimental and theoretical R&D for next-generation distributed fiber-optic acoustic sensing systems.
* Develop, implement, and validate DSP algorithms for coherent optical sensing systems.
* Build end-to-end expertise across the full sensing pipeline, including optical signal generation and propagation, coherent detection, signal acquisition, DSP, and advanced data analysis.
* Design and run laboratory experiments to validate new concepts, improve system performance, and support current and future product generations.
* Develop simulations, models, and analysis tools using Python and/or C ++ for lab experiments, field data analysis, signal processing, and system -level performance evaluation.
* Work with field experiment data to identify performance gaps, validate models, and improve sensing capabilities.
* Collaborate with R&D teams across disciplines to develop, validate, and integrate new sensing capabilities.
* Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

We are seeking an AI Networking Exploration Architect for our Networking Insights Group to bridge the gap between cutting-edge, hyper-scale AI workloads and the datacenter infrastructure that enables them. You will join a small, focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding and end-to-end systems thinking. Your insights will directly shape our products across the full stack-from applications and software libraries to hardware architecture and physical design.

What You'll Be Doing:

Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.

Translate state-of-the-art research into actionable infrastructure, software, and hardware features in partnership with architecture teams.

Rapidly master new AI domains (LLMs, generative models, multimodal systems) and distill key findings for product teams.

Incorporate your deep knowledge of AI applications into our hardware and software roadmaps.

Conduct independent research by formulating hypotheses about workload behavior and validating them through rigorous analysis.

Drive architectural innovation and network optimization by applying your domain expertise to exploratory analysis of real-world Deep Learning (DL) workloads.

The MLIL DataPlane team is looking for a Senior Software Development Engineer to own the design and implementation of our inference data plane. We build the software that makes large models run efficiently on custom hardware - spanning model execution, memory management, data movement, and serving integration.
Our work covers the full inference path: integrating serving engines with custom hardware, developing high-performance compute kernels, enabling efficient data movement, and driving models from early validation through production. We operate at frontier scale with large distributed models.
This is a ground-up effort with rapidly evolving hardware and software. We need a senior IC who can write and optimize low-level code for custom hardware, validate model architectures end-to-end, build test and profiling infrastructure, and drive performance across the stack.

Key job responsibilities
- Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference.
- Implement and validate LLM architectures (decoder-only, mixture-of-experts) end-to-end - from PyTorch model definition through distributed execution on custom hardware.
- Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism.
- Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets.
- Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bringup.
- Own features end-to-end: from design through implementation, testing, and integration into the broader software stack.
- Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions.
- Mentor engineers, drive design reviews, and raise the engineering bar across the team.

Our breakthrough World Model technology combines aerial imagery with real-time ground views to build an accurate, rich representation of the vehicle's environment. As part of our World Modeling team, you will drive the next generation of AI-powered, cloud-defined vehicle technology-tackling ambitious research and turning cutting-edge deep learning into real-world solutions.

Responsibilities
Research, design, and implement state-of-the-art deep learning models in computer vision (e.g., detection, segmentation, transformers, self-supervised learning).
Translate research into production-ready algorithms, writing clean, efficient, and scalable code in Pytorch.
Explore creative ideas, experiment with novel architectures, and continuously push beyond current SOTA.
Take ownership of your work end-to-end, from concept through prototyping to deployment in production.

We are looking for a talented, self-driven, and hands-on DSP Researcher to join R&D team.
In this role, you will research and develop next-generation distributed fiber-optic sensing technologies, focusing on signal processing, system modeling, simulations, and experimental validation. Working at the intersection of physics, electro-optics, fiber-optic sensing, and DSP, you will transform complex physical phenomena into robust sensing solutions for real-world applications.
You will tackle challenging physical and algorithmic problems, contribute to cutting-edge sensing products, and collaborate closely with multidisciplinary R&D teams.
This role is ideal for someone who thrives in a fast-paced environment, enjoys working across disciplines, and is excited to take ideas from research and lab validation to large-scale deployment.
What Youll Do:
Conduct experimental and theoretical R&D for next-generation distributed fiber-optic acoustic sensing systems.
Develop, implement, and validate DSP algorithms for coherent optical sensing systems.
Build end-to-end expertise across the full sensing pipeline, including optical signal generation and propagation, coherent detection, signal acquisition, DSP, and advanced data analysis.
Design and run laboratory experiments to validate new concepts, improve system performance, and support current and future product generations.
Develop simulations, models, and analysis tools using Python and/or C++ for lab experiments, field data analysis, signal processing, and system-level performance evaluation.
Work with field experiment data to identify performance gaps, validate models, and improve sensing capabilities.
Collaborate with R&D teams across disciplines to develop, validate, and integrate new sensing capabilities.
Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Req ID: 29153

As a Senior Machine Learning Scientist, you will work closely with engineers and product to design, develop, evaluate and deploy Gen AI powered solutions for scalable, customer-facing applications. Your work will focus on applying state-of-the-art agentic capabilities and driving business impact based on rigorous evaluations and experimentation.


Key Job Responsibilities and Duties:

Design end-to-end agentic systems, delivering high-quality, performant, and efficient solutions to production

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Pioneer and promote best practices and adoption of new technology in GenAI application development

Lead and mentor other team members, providing technical guidance and timely feedback to develop the team and motivate them to achieve their goals.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

We are looking for a Senior Software Engineer to join the AIOps platform team and help build the core distributed systems that ingest massive telemetry streams from GPU clusters and operationalize predictive AI models at scale. You will work at the intersection of high-performance data engineering and production ML, turning research algorithms into reliable, mission-critical software.

What you'll be doing:

Architect and build an agentic AIOps system that autonomously monitors GPU fleet health, aggregates and correlates massive telemetry streams, surfaces intelligent alerts, and orchestrates multi-step diagnostic workflows and corrective actions - powering real-time dashboards, automated root-cause analysis, and proactive incident response.

Research, evaluate, and prototype data storage strategies and data representations across diverse database technologies and modalities, ensuring AI models are trained on high-quality, well-structured data that improves predictive accuracy and generalization.

High-Scale Engineering: Design distributed systems to handle the extreme telemetry density of large-scale AI clusters, ensuring efficient data ingestion, processing, and real-time analysis.

Instrument services with deep observability (metrics, logs, traces) to support rapid debugging and continuous performance improvement.

Build and own the model-serving infrastructure that operationalizes predictive algorithms at scale - packaging, versioning, deploying, and monitoring AI models in both SaaS and on-premises environments.

Contribute to the platform's core libraries and abstractions that accelerate development across the broader AIOps engineering team.

At our company, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we pioneered the world's first fully automated suite of AI-powered vehicle inspection systems, combining computer vision, machine learning, and generative AI. With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone.
We are looking for an experienced Research Scientist, Sensing to push UVeyes inspection beyond todays sensing stack. You will evaluate, prototype, and develop AI models for new sensing modalities such as thermal, hyperspectral, acoustic, radar, structured light, and event cameras, surfacing vehicle defects that todays systems cannot see.
A day in the life and how youll make an impact
* Survey emerging sensing technologies and identify the modalities most likely to expand UVeyes defect coverage.
* Prototype with new hardware, build small-scale datasets from scratch, and train modality-specific models.
* Run controlled experiments that benchmark new modalities against todays inspection stack on shared defect scenarios.
* Develop multi-modal fusion approaches that combine new and existing sensors for stronger detection.
* Recommend which modalities graduate into product and partner with R&D and product on integration.

we are looking for a Director of AI.
The Director of AI will lead the companys AI organization, including algorithm development teams, embedded algorithm engineers within product squads, and the Data Operations team.
This role combines strategic leadership with execution ownership, driving the development, deployment, and continuous improvement of AI-powered products across power transmission, border security, and critical infrastructure monitoring.
The Director of AI is accountable not only for algorithm innovation, but also for ensuring successful production deployment and measurable customer impact. This leader will establish a customer-first culture across the organization, aligning AI development priorities with customer needs, product objectives, and business outcomes.
The ideal candidate is a visionary yet execution-oriented leader who can bridge cutting-edge AI research with scalable production systems, build high-performing teams, and deliver measurable customer value.
Success in this role will be measured not only by algorithm performance, but by the ability to deliver AI capabilities into production and generate measurable value to our customers.
What Youll Do:
Lead the AI group, including algorithm development teams, embedded algorithm engineers, and data operations.
Build and mentor high-performing teams, fostering a culture of ownership, collaboration, continuous improvement, and customer focus.
Define and execute the AI roadmap in alignment with company strategy, customer needs, and product objectives.
Lead the development and deployment of AI, Machine Learning, Deep Learning, and Signal Processing solutions.
Own the full AI lifecycle, from research and experimentation through production deployment, monitoring, and continuous optimization.
Lead the Data Operations function, ensuring efficient annotation, labeling, and quality processes that support AI development.
Partner closely with Product, Engineering, and business leaders to maximize AI impact across the organization.
Engage with customers and stakeholders to understand challenges, identify opportunities, and drive measurable business value.
Communicate AI strategy, progress, opportunities, and risks to executive leadership.
Contribute to company strategy, product direction, and technology leadership.