Senior AI and Machine Learning Specialist

Posted 5 Days Ago
Be an Early Applicant
Yokneam, ISR
In-Office
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Lead design, build, deploy, and operate production AI systems across classical ML, computer vision, LLMs, and agentic workflows. Create scalable model-serving and workflow architectures, evaluation strategies, observability and feedback loops, optimize inference and cost, and provide technical leadership, code reviews, and mentoring.
Summary Generated by Built In

We are hiring Senior AI / Machine Learning Engineers to compose, build, and operate production AI systems across classical machine learning, computer vision, large language models, and agentic workflows. You will work across the full AI engineering lifecycle, from initial development and evaluation to deployment, observability, and ongoing improvement. This role suits engineers who can switch easily between system architecture and hands-on implementation. It is for those who understand what it takes to make AI systems reliable at production scale.

What you’ll be doing:

  • Lead the build and delivery of production AI systems across machine learning, computer vision, LLM, and agentic use cases.

  • Build AI applications and agents that use tools, complete multi-step workflows, maintain state, and operate safely in production.

  • Develop evaluation strategies, test suites, quality metrics, and production feedback loops for models and AI applications.

  • Build scalable architectures covering model serving, APIs, data flows, workflow orchestration, observability, security, and failure recovery.

  • Build durable, distributed workflows using platforms such as Temporal, Prefect, or comparable technologies.

  • Deploy, monitor, and continuously improve AI systems for quality, latency, efficiency, reliability, scalability, and cost.

  • Make informed technical decisions around model selection, inference architecture, context management, structured outputs, tool use, and infrastructure.

  • Establish effective development, deployment, and validation practices for services, models, workflows, and infrastructure And provide technical leadership through architecture reviews, build decisions, code reviews, mentoring, and engineering guidelines.

What we need to see:

  • 5+ years of experience in machine learning engineering, AI engineering, software engineering, platform engineering, or a comparable production-focused role.

  • Bachelors degree

  • A solid history of advancing innovative AI or machine learning systems from prototype to production.

  • Extensive knowledge in one or more fields including classical machine learning, computer vision, NLP, generative AI, or LLM applications.

  • Strong system-design skills, including experience with distributed systems, data-intensive applications, and cloud infrastructure.

  • Practical understanding of production LLM inference, including latency and efficiency trade-offs, context windows, token usage, model selection, and cost management.

  • Experience working with containers, orchestration platforms, CI/CD, monitoring, observability, and production incident investigation.

  • Sound engineering judgment around scalability, reliability, security, maintainability, and operational complexity.

  • The ability to independently guide complex technical projects and make effective decisions in ambiguous environments.

  • Strong communication and collaboration skills, including the ability to explain technical trade-offs to engineers, product teams, customers, and other collaborators.

Ways to stand out from the crowd:

  • Experience working with both traditional machine learning systems and contemporary LLM or agentic applications.

  • Excellent judgment about when agent-based approaches are appropriate—and when a simpler solution is more effective.

  • Experience making AI behavior measurable, observable, explainable, and safe in production.

  • Experience optimizing inference systems for performance, infrastructure efficiency, and operating cost.

  • A history of guiding engineers or heading cross-departmental technical projects.

We are looking for engineers who care deeply about technical quality. They take ownership from building through production. They are motivated by the challenge of turning advanced AI capabilities into reliable products.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Skills Required

  • 5+ years of experience in machine learning engineering, AI engineering, software engineering, platform engineering, or comparable production-focused role
  • Bachelor's degree
  • Proven history of advancing AI or machine learning systems from prototype to production
  • Extensive knowledge in one or more fields: classical ML, computer vision, NLP, generative AI, or LLM applications
  • Strong system-design skills for distributed systems, data-intensive applications, and cloud infrastructure
  • Practical understanding of production LLM inference, including latency, efficiency, context management, model selection, and cost management
  • Experience with containers, orchestration platforms, CI/CD, monitoring, observability, and production incident investigation
  • Ability to guide complex technical projects independently and make effective decisions in ambiguous environments
  • Strong communication and collaboration skills to explain technical trade-offs to engineers, product teams, and customers
  • Experience building durable, distributed workflows using platforms such as Temporal, Prefect, or comparable technologies

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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