BioMedical AI Research Engineer

About the role

It goes beyond traditional LLM plus RAG pipelines by building intelligent agents embedded within discovery workflows.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

Requirements

The posting states a bachelor's degree requirement. Hold a degree in Computer Science, Machine Learning, Computational Biology, Biomedical Informatics, or a related field.

Demonstrate strong hands-on experience with large language models, including fine-tuning, alignment, and structured prompting.

Show experience building agent-based systems or complex LLM orchestration frameworks such as multi-agent systems, tool-using LLMs, and planning modules.

Exhibit proficiency in Python and modern ML frameworks including PyTorch, JAX, and TensorFlow.

Have experience working with large-scale biomedical datasets such as genomics, transcriptomics, clinical records, or scientific corpora.

Nice to have

Prefer experience designing autonomous research assistants or AI systems that perform multi-step scientific reasoning.

Show familiarity with knowledge graphs, hybrid symbolic-neural systems, or multimodal foundation models.

Demonstrate understanding of privacy, security, and regulatory considerations in healthcare AI.

Skills & tools

Core skills include agentic AI design, LLM fine-tuning, and multi-step reasoning.

Primary tools involve Python, PyTorch, JAX, TensorFlow, and related ML frameworks.

Knowledge of biomedical data types such as genomics, transcriptomics, clinical records, and scientific literature is relevant.

Practical notes

Xaira Therapeutics is an equal-opportunity employer that values diverse and inclusive teams.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

The role focuses on agentic AI systems for biomedical discovery and relies on modern LLM techniques.

Work involves integrating complex, heterogeneous data sources into autonomous workflows.

Success requires strong engineering rigor for building scalable, production-grade AI systems.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.