# Applied AI Engineer at PhaseV

> Location unclear · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Aphasev%3A4106268009 (read 2026-11-11).

## Facts (observed)

- Company: [PhaseV](https://alderhq.app/companies/phasev)
- Location text: Dereck Ha'SHalom St 4; TLV Office
- Where it can be done from (inferred): Location unclear (evidence: "Location: Dereck Ha'SHalom St 4; TLV Office")
- Posted: 2026-01-15 (299 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/phasev/jobs/4106268009
- Skills listed (inferred): Python, LLMs

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

### Position Overview

PhaseV is a technology company that redefines clinical development at scale with its enterprise-ready, multi-modal AI/ML platform. With over $65M raised from top investors, we are poised to make a significant impact on how drugs are developed and brought to market - ultimately helping effective treatments reach patients faster.

We are seeking a talented and driven Applied AI Engineer to join our team. This is a unique opportunity to have a direct and real-world impact using AI by helping redefine how clinical trials are designed, optimized, and run. As an Applied AI Engineer, you will also join a high-caliber, multidisciplinary, cross-functional team spanning engineering, data science, and product.

This role sits at the intersection of applied AI and product development, with a strong focus on building, deploying, and supporting production-grade AI systems that operate at scale. The ideal candidate will have a strong foundation in NLP, LLMs, and agentic systems alongside proven experience building AI-powered services.

### Key Responsibilities

### Technical Development

- Build and maintain scalable, production-grade generative AI pipelines that integrate updating unstructured and structured data.
- Research and develop robust agentic systems to solve complex clinical problems.
- Develop evaluation frameworks for AI systems in clinical contexts.
- Optimize representation and retrieval strategies to support explainable outputs.
- Contribute to architectural decisions around AI services, APIs, and model orchestration frameworks.
### Research & Analysis

- Stay up-to-date with the latest developments in AI, machine learning, and related fields, exploring how emerging technologies can be applied to improve products and services.
- Document and present methodologies and results for internal and external stakeholders.
### Collaboration

- Work cross-functionally with engineering, data science, and product teams.
- Contribute to technical discussions and peer code reviews.
- Support the preparation of technical documentation and research papers.
### Qualifications

### Education

- Master's in Computer Science, Data Science, Engineering or a related field is an advantage but not required
- Ph.D. is an advantage but not required
### Experience

- Hands-on experience building agentic workflows in production environments.
- Strong understanding of NLP, LLMs, entity recognition, and evaluation techniques.
- Experience designing and using embedding-based representations, including similarity search and retrieval workflows.
- Proven experience working with unstructured data (e.g., free text, documents, notes) and applying techniques to transform it into structured formats.
### Skills

- Strong programming skills in Python.
- Ability to clearly communicate complex technical concepts.
- Experience with data visualization.
- Strong analytical and problem-solving skills.
- Ability to work in a dynamic, fast-paced environment.
### Preferred

- Experience in the healthcare or biotech industry.
- Experience with clinical data.
- Experience with large-scale data processing.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aphasev%3A4106268009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.