דרושים » תוכנה » Senior Machine Learning Engineer, Foundational Models and Agentic systems

We are seeking a visionary and hands-on Staff AI Scientist to help shape the next generation of foundational language models and agentic systems that empower small and medium-sized businesses to thrive. In this role you will define, design, and build groundbreaking AI solutions that understand, reason, and act across complex financial and business contexts.

You will collaborate closely with cross-functional experts across AI research, data science, engineering, and product to create intelligent agents that help SMBs automate financial tasks, make smarter decisions, and operate more efficiently. Your work will combine scientific rigor with visionary execution, balancing theoretical innovation with large-scale practical impact.

As a Staff Scientist you set technical direction beyond your own projects. You are a builder-scientist - a creative thinker who develops novel model architectures and training paradigms and also drives their implementation and validation in real-world applications - and you raise the scientific bar for the teams around you. You will help shape the AI foundation that powers transformative products serving millions of customers.


Responsibilities

Set technical direction. Define the technical strategy and roadmap for next-generation agentic and foundation-model systems, and drive innovation and scientific excellence across the organization.

Build foundational models and agents. Design and develop foundational language models and agentic architectures that power intelligent decision-making for small and medium businesses.

Advance the science. Explore novel approaches in large-scale pretraining, fine-tuning (SFT and RLFT/GRPO), retrieval-augmented generation, and reasoning to improve factuality, adaptability, and alignment.

Solve real financial work. Build intelligent agents that understand financial data, customer context, and business workflows - from bookkeeping and autonomous accounting to planning, communication, and insight generation.

Leverage Intuits data moat. Turn Intuits unique ecosystem signals - counterparty behavior, two-sided transaction visibility, and business twins - into defensible modeling advantages.

Ship end-to-end. Take work from experimentation to production, owning evaluation frameworks and metrics that prove real-world impact.

Elevate others. Mentor scientists and engineers, review technical designs, and grow the craft of the wider team - a core expectation at the Staff level.

Collaborate across functions. Partner with AI research, engineering, product, and design to translate breakthrough science into scalable products used by millions.

Share knowledge. Publish and present research findings, internal innovations, or contributions to open-source frameworks where appropriate.

We're looking for a Senior AI Solutions Builder to join our Product team. In this role, you will design, build, and ship AI-powered solutions that enhance productivity and drive measurable business value across the organization.
You will operate at the intersection of AI, data, pricing and business - owning the end-to-end development of agents, copilots, and workflow automations that directly inform decisions and eliminate manual work.
You are both a builder and an enabler. You ship solutions yourself and raise the AI capability floor for the teams around you. You'll collaborate closely with Product, Engineering, CS, Sales, and builders across the company to deliver outcomes that improve decision velocity, product and operational efficiency, and long-term growth.
You Will:
Design and ship AI-powered copilots, agents, and decision-support tools for the Product team, starting with Pricing
Build data pipelines and analytical models for pricing intelligece
Develop autonomous agents that automate recurring Product tasks
Automate manual workflows across product tools so the right action happens when a signal fires
Build LLM-based scoring models that give roadmap and prioritization decisions a data backbone
Enable and upskill product teams through AI playbooks, sessions, and hands-on co-building
Collaborate with builders across Engineering, CS, GTM, and other teams to co-create solutions that scale
Define and track impact: time-to-insight, decision velocity, manual hours eliminated, and business outcomes

Required Staff Data Scientist
Job Description
As a Staff Data Scientist, youll play a highly embedded and influential role within our Data Science organisation, partnering closely with Development and Product teams across multiple products within ITOM. Rather than simply responding to predefined requirements, youll bring your own creativity, domain expertise, and ideas to identify opportunities, challenge assumptions, and solve complex problems using real customer data.
What you get to do in this role:
Lead the development and evolution of advanced data science and machine learning capabilities, including noise reduction, anomaly detection, root cause analysis, and new AI-powered product features.
Own complex data science problems end-to-end, from problem definition and ideation through experimentation, model development, evaluation, productionization, and deployment.
Develop advanced algorithms and models using techniques such as machine learning, deep learning, NLP, anomaly detection, and statistical modeling.
Partner closely with Product, Engineering, Quality, and Architects to translate product requirements into scalable, reliable ML solutions.
Drive technical direction and mentor other data scientists, contributing to best practices in modeling, experimentation, evaluation, and ML engineering.
Stay current with advances in AI/ML and identify opportunities to apply emerging technologies to our products.

we are looking for an experienced Data Scientist to join us and take a key role in turning large-scale, real-world data into smarter product and business decisions.With millions of users interacting with mobility services, our data reflects complex customer behavior, business models, and real-life mobility patterns.We're looking for someone who can go beyond building models - someone who knows how to identify the right problems, challenge assumptions, uncover opportunities in the data, and translate them into real business impact.
You'll have significant professional ownership, from taking an ambiguous business question and framing it as a statistical or Machine Learning problem, through choosing the right methodology, building and evaluating the solution, and bringing it to production.Just as importantly, we expect you to proactively identify questions that should be answered with data - even when nobody has asked them yet.
Responsibilities:
Own Data Science problems end-to-end - from business question and hypothesis to modeling, evaluation, and production.
Partner closely with Product, Business, Data, and Engineering teams to translate complex business challenges into data-driven solutions.
Proactively identify opportunities where statistical analysis, Machine Learning, and AI can improve our products, customer experience, and business performance.
Build, train, evaluate, and improve Machine Learning models used in production, across both real-time and batch use cases.
Select the right statistical or ML approach based on the problem and the data - including recognizing when a simple analytical solution is better than a complex model.
Define meaningful evaluation methodologies and connect model performance to real business impact.
Communicate complex statistical findings clearly to both technical and non-technical stakeholders.

We are looking for a SW Engineering Manager to manage and mentor a team of engineers developing AI-based Agents. In this role, you will combine hands-on technical leadership with strong execution, ensuring high-quality delivery across multiple agent projects. You will drive development processes, maintain clear communication with stakeholders, and support the team through the full lifecycle of agent development, from design to production and beyond.
Lead, mentor and grow a team of AI-agent developers; foster a collaborative and a high-performance environment.
Own team execution: sprint planning, priorities, delivery commitments, and production support.
Oversee end-to-end development of AI agents: design, implementation, integration, deployment, monitoring and continuous improvement.
Maintain a clear capability matrix and roadmap across multiple agents and integrations.
Communicate progress, risks, and status updates clearly to product, management, and cross-functional teams.
Provide hands-on technical leadership: review code, support architectural decisions, and uphold engineering best practices.