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join the revenue team, a prominent tech company specializing in automotive solutions traded on nasdaq. what will your job look like: building & analysis of short/long term business plans.
analyzing the business activity and trends.
building and maintaining the company's long-term plan and other ad - hoc models to support strategic decision making.
conducting quarterly rolling forecast and insightful analysis of company's revenues and cost of sales (product mix, gross margin etc.)
manage budget control and analysis to maintain expenses control.
preparing detailed budget with budget owners/senior and providing quarterly reports of usage.
performing a quarterly analysis report: actual vs. forecast and budget.
participating in quarterly closing process (provisions, analysis, monthly reports and presentations).
maintain the companys long-term forecast.
working with accounting, budget systems and related sox controls.

our CTO group is looking for an outstanding physical-ai applied researcher to join our team.
the CTO group is a small, elite research unit shaping the next generation of algorithmic foundations behind our autonomous driving systems. the group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy. we are seeking a researcher who thrives at the intersection of Machine Learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems. in this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. you will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. the problems are open-ended, scientifically challenging, and deployed at unprecedented scale. this is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. if youre excited about pushing the boundaries of learning-based decision-making, wed love you to join us and help shape the future of physical ai at our company. what will your job look like:
design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
lead research directions from conception to full-scale production.
develop using Python (pytorch or similar frameworks) as well as C ++/gpu/cuda.

join our advanced packaging team to define and develop cutting-edge semiconductor package solutions for automotive-grade digital and RF system -on-chip ( SOC ) products.
youll collaborate with internal teams and external osats to deliver high-performance, manufacturable, and reliable packages that meet stringent automotive standards.
what will your job look like:
define and specify advanced package architectures for digital socs and RF ics in automotive applications.
perform substrate design for flip-chip bga (fc-bga) packages, including stack-up, routing, and ball-out optimizations.
evaluate and select substrate technologies, materials, and package solutions based on performance, cost, and reliability.
collaborate with osats to define and validate assembly materials and process flows.
work closely with internal teams such as backend, sipi, product engineering, and quality & reliability to ensure seamless integration.
monitor yield, quality, and manufacturability across package development and production stages.
support dfm reviews and ensure compliance with automotive standards and customer requirements.