Description review
Xaira Therapeutics | Midlevel/Senior/Staff Engineers | Seattle or SF
Xaira Therapeutics · Seattle or SF · back to the listing
HR standards
43/100
poor
Title ↔ description
49/100
poor
Reads as
Machine Learning Engineer
96% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Xaira Therapeutics is developing biological foundation models (not LLMs) to hit undruggable targets and cure disease. We've put together an incredible cross-disciplinary team of pharma veterans who have brought drugs to market, drug designers/AI scientists from Nobel laureate David Baker's lab, and engineers from tech in order to meet the moment and build a company to reimagine how drugs are made in the pursuit of better medicines. We're not a SaaS company, and the ultimate goal is to see the medicines we make delivered to patients.
I lead the engineering team and am hiring across multiple teams. There's:
- Research Engineering: work on a modeling team with the goal of making a model better. You think in tensors, understand what makes a kernel fast, and obsess over training throughput.
- X-Scientist: building an agentic copilot for drug development with access to our internal suite of tools. You have backend experience, have built tools for agents to interact with, and enjoy tinkering with agent harnesses.
- Data Engineering: we generate large volumes of data from our lab that provide critical feedback loops to our modeling efforts. You either hate or love lakehouses, have a preferred ETL orchestrator, and are intrigued by the problem of capturing data off of lab instruments.
- ML Platform: boy do we have some GPUs for you. Modeling teams need productive compute infrastructure, job scheduling, and numerous other affordances so that they can "just focus" on the model. You shudder at the words "falling off the bus", you have an opinion on how checkpoints should be recorded, and you've built tools that improve the modeling lifecycle.
Send me an email to apply: [email protected]
Include a resume and tell me about something you've built that illustrates your skills.
I lead the engineering team and am hiring across multiple teams. There's:
- Research Engineering: work on a modeling team with the goal of making a model better. You think in tensors, understand what makes a kernel fast, and obsess over training throughput.
- X-Scientist: building an agentic copilot for drug development with access to our internal suite of tools. You have backend experience, have built tools for agents to interact with, and enjoy tinkering with agent harnesses.
- Data Engineering: we generate large volumes of data from our lab that provide critical feedback loops to our modeling efforts. You either hate or love lakehouses, have a preferred ETL orchestrator, and are intrigued by the problem of capturing data off of lab instruments.
- ML Platform: boy do we have some GPUs for you. Modeling teams need productive compute infrastructure, job scheduling, and numerous other affordances so that they can "just focus" on the model. You shudder at the words "falling off the bus", you have an opinion on how checkpoints should be recorded, and you've built tools that improve the modeling lifecycle.
Send me an email to apply: [email protected]
Include a resume and tell me about something you've built that illustrates your skills.