לוח משרות דרושים NLP/Machine Learning, לחיפוש זה נמצאו 88 משרות

we are looking for a ML Engineer.
As a Senior ML Infrastructure Engineer, you will design and build the infrastructure that powers our ML and LLM systems in production. You will develop scalable pipelines, systems, and tools that enable data scientists and AI teams to efficiently develop, test, and deploy models.
Working closely with data scientists, engineers, and product teams, you will ensure our ML capabilities are reliable, scalable, and production-ready-helping bring cutting-edge AI to improve mental health care.
This is a unique opportunity to join a startup with a real impact on thousands of peoples wellbeing and mental health, applying cutting-edge AI technologies to solve meaningful human problems.

We are looking for a Senior Engineer with a data engineering background to join our growing ML Platform team. This is a great opportunity, whether you have experience with ML and are looking for a ML focused product or are an experienced Data Engineer looking to enter the world of ML. Together well provide tools to develop more effective models, get them into production faster, and ensure that they continue to perform well over time.
ML is central to our work. It enables us to process billions of $ worth e-commerce transactions, make decisions in real time, identify fraud rings, and quickly detect new attack methods. Precision is crucial - bad decisions by our models cost us directly and put money into the pockets of fraudsters.
Our adoption by merchants around the world provides us with billions of fresh data points each day. Our team of data scientists, analysts, and cyber intelligence specialists continually identify new signals, engineer new features, and research new models. But as the volume of data and the number and complexity of models grows, so do the engineering challenges.
If this kind of working environment sounds exciting to you, if you understand that Engineering is about building the most effective and elegant solution within a given set of constraints - consider applying for this position.
Why should you join us?
Youll be part of a highly proficient engineering team that is a focal point for all ML engineering activity, striving to constantly bring innovation and leverage ML capabilities across all company teams and products.
This role presents a unique opportunity to enter the ML domain. For those already experienced in ML infrastructure, it offers the chance to grow within a team that specializes in high-scale, Big Data and ML systems.
What you will be doing:
Designing, building, and maintaining the ML infrastructure that allows our models to make billions of real-time decisions every year.
Building a platform that enables managing a full ML model lifecycle - from researching to training, deploying, and serving predictions in real-time.
Building distributed data processing pipelines to support model development.
Acting as a consultant to researchers, data scientists, and expert analysts and enabling them to research new models faster and with greater precision by providing cutting-edge tooling.
Expanding our ML infrastructure to make it scalable, quick, and efficient to bring diverse models to production and to monitor their performance and drift over time.
Expanding the pool of internal customers able to use ML. Work with them to understand their needs and help them make the most of the infrastructure that well provide.
Acting as an advocate for MLOps, continually improving our processes, and raising our standards.

Alice is building its internal AI infrastructure layer from the ground up. We have real agents running in production, a growing base of employees using AI in their daily work, and a clear architectural direction. What we don't have yet is a dedicated engineer to own it. You'll be the first. Your job is to close the gap between "working prototype" and "production platform" - owning the foundation that hosts our agents, the pipelines that ship them, and the reliability layer (observability, cost controls, audit trails, evals) that makes it safe to run AI at scale in a trust & safety company. This is an infrastructure-first role with deep AI fluency - not a prompt engineer, not a wrapper-framework operator, not a no-code builder. You should be equally comfortable writing a Terraform module, debugging a Kubernetes pod, and tracing an agent's tool-call chain. We don’t operate with a predefined backlog here; you will be responsible for identifying high-impact needs and bringing them to life. The perfect fit for this role has a track record of deploying agentic systems that have held up under real-world usage, balances a focus on infrastructure with a deep concern for user experience, and recognizes that the primary hurdle in AI integration is rarely the model itself. Responsibilities: Platform & Infrastructure
* Architect, build, and run the AWS/Kubernetes platform that hosts Alice's internal AI agents and tools; drive AWS Well-Architected pillars (operational excellence, security, reliability, performance, cost, sustainability).
* Own Infrastructure-as-Code: Terraform modules, standards, and reviews for Bedrock, agent runtimes, vector DBs, and supporting services. AI Systems
* Design and ship production-grade agents and multi-agent pipelines using the Anthropic Agent SDK, Claude Code, AWS Bedrock, and MCP — not wrapper frameworks.
* Own the full agent lifecycle: scoping ? prototyping ? eval ? deploy ? monitor ? iterate.
* Integrate agentic workflows into internal and product systems via APIs, databases, webhooks, Slack, and email. Reliability, Observability, Cost
* Build first-class observability across apps and infra: OpenTelemetry, Prometheus, plus LLM-specific tracing (Langfuse or equivalent), token/cost metrics, and eval pipelines.
* Define SLOs/SLIs and error budgets for AI services - latency, model fallback chains, eval regression gates, agent success rates. Lead incident readiness, response, and post-mortems.
* Drive FinOps: model routing by cost, cache hit rates, batch vs. realtime tradeoffs, budget alarms, per-team chargeback visibility.
* Implement guardrails: prompt-injection defenses, PII redaction, model allowlists, human-in-the-loop checkpoints, audit trails. Org Impact
* Identify high-leverage workflows across the organization and translate them into scalable agentic automations.
* Partner with R&D, Delivery, security, and external vendors to deliver platform capabilities.

About Alice:
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact—whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

Hybrid:
Yes