How to Hire a Machine Learning Engineer for a Pharma Team

Companies want AI, and the people to build it are the bottleneck. The Pistoia Alliance's 2025 Lab of the Future survey of more than 200 life science experts found that 77% expect to use AI in their labs within two years. In the same survey, 34% named a lack of skilled people as a barrier to AI adoption, up from 23% the year before. These are lab and R&D respondents, so read the figure as a signal for research teams, not a count of open jobs.

Skills are not the only drag. The same survey found data silos still the top challenge to making the best use of lab data, at 57%, and only 9% of respondents saw regulation as a barrier to AI, down from 23% a year earlier. For a hiring manager that points two ways. A new engineer can stall without access to the data, so check that before you hire. And the regulatory documents below are about doing the work well, not a wall standing in front of it.

Benchling's 2026 Biotech AI Report adds a hiring detail. It surveyed about 100 biotech and biopharma organizations that already use AI, in November 2025. Internal upskilling was the top source of AI talent at 67%, well ahead of hiring from the tech industry at 21%. The report's own words for the target profile are "hybrid scientists" who bridge models, data and experiments, and it quotes Karen Akinsanya of Schrödinger naming talent as the biggest challenge.

Government data is thin for this exact job title. We could not find a US Bureau of Labor Statistics line for machine learning engineers, so the closest published occupation is data scientists: projected growth of 35% from 2025 to 2035, about 24,800 openings a year and a median wage of $120,230 in May 2025. That covers every industry, not pharma, so treat it as a sign of how much competition a pharma team faces.

Sources: Pistoia Alliance, Lab of the Future 2025 survey release (September 2025); Benchling, 2026 Biotech AI Report; US Bureau of Labor Statistics, data scientists.

A machine learning engineer builds the path from a trained model to a system people rely on, then keeps it working. In pharma, two things change that job: the data comes out of experiments or regulated processes, and some models end up informing decisions that regulators care about.

Regulators have started to say what they expect. In January 2025 the FDA issued draft guidance proposing a risk-based way to judge whether an AI model is credible for a particular context of use, meaning the specific question the model is there to answer. It applies to AI that supports regulatory decisions about a drug's safety, effectiveness or quality. FDA's own guidance page still labels it a draft and "not for implementation", so treat it as direction, not rule. In January 2026 the FDA and the European Medicines Agency then published ten shared principles for good AI practice in drug development. Several read like a job description: "Clear context of use", "Multidisciplinary expertise", "Data governance and documentation" and "Life cycle management".

For regulated manufacturing and quality systems, industry guidance points the same way. ISPE published its GAMP Guide: Artificial Intelligence in July 2025, written to be used alongside GAMP 5 (Second Edition) for AI-enabled computerized systems in GxP areas, with patient safety, product quality and data integrity as the stated aims.

Not every ML role sits under these expectations. A model that ranks compounds in early research faces different scrutiny from one that supports a batch release decision. Ask your quality or regulatory team which one your role is before you write the job description.

Sources: FDA, draft guidance page and FDA announcement of the draft (January 2025); FDA and EMA, guiding principles of good AI practice in drug development (January 2026); ISPE GAMP Guide: Artificial Intelligence (July 2025).

The table turns the documents above into a screening list. The "why" column is our reading of those sources, not a quotation from them.

These are our suggestions, built from the skills above. They are not a standard and they will not fit every role. Ask them in conversation, and listen for specifics.

1. "Tell me about a model you took to production. What changed after launch?" Listen for monitoring, a rollback plan and a named person who owned it.
2. "How was the training data produced, and what would worry you about it?" Strong candidates ask about the instrument, the batch and the lab before they talk about algorithms.
3. "What would you write down before this model supports a decision someone else signs off?" Listen for intended use, limits, test results and who reviews them.
4. "How would you tell whether a retrained model behaves differently in a way that matters?" Listen for comparison against fixed test data and a clear threshold for escalating.
5. "A scientist says your result doesn't match the assay. What do you do first?" The best answers start with questions for the scientist, not a defense of the model.

The Benchling numbers say most companies grow AI talent from the inside, and Pistoia's respondents asked for help doing it: 51% want best-practice guides, 45% want AI and ML courses and 40% want skills training. Upskilling works when you have scientists who already code, a senior engineer who can mentor them and some patience. It struggles when nobody on the team has shipped a production model before, because upskilling needs someone to learn from.

A rough rule: upskill when the gap is domain-to-code, hire when the gap is engineering experience, and expect to do both when the project is large. A first engineering hire can also be the person who trains the scientists around them.

Whichever route you pick, sort out the data first. Before the search opens, write down where the training data lives, who can approve access to it and how long that takes. Candidates will ask, and the honest answer shapes who accepts your offer.

If you decide to hire, the profile is narrow and the pool is small and in demand. That is where a specialist recruiter earns its fee, and we explain how those fees work in our guide to recruitment agency fees in pharma and biotech.

Geeqers is a US recruiting firm for pharma, biotech and medical-device companies, and AI and machine learning engineers are among the roles on our employer page. This is how a search works with us, as we describe it on that page:

• We screen every candidate ourselves. We search the market and speak to each person before you hear their name.
• A shortlist within 7 business days of a complete brief. For a rare profile the list may hold two people instead of five, and we tell you that on day zero.
• No fee until your hire starts work, and nothing if nobody starts. The fee is agreed with you in writing before we begin.
• A 90-day replacement. If your hire resigns, or you let them go for performance reasons, within 90 calendar days of their start date, we run the search again at no extra fee. The conditions are in the agreement you sign first.
• Confidential searches on request, useful when a new AI hire signals a strategy you have not announced.

If you have a role to fill, send us a hiring brief. Tell us whether the model sits in research or in a regulated process, and who will interview the candidate on the science side.

What does a machine learning engineer do at a pharma company?

They build, deploy and monitor models that scientists or regulated processes depend on. The work ranges from research tools in discovery to systems that support quality or manufacturing decisions, so define the context before you define the job.

Do pharma machine learning engineers need GxP experience?

It depends on where the model sits. If it supports a GxP process or a regulatory decision, working knowledge of validation and data integrity is worth requiring. For early research, willingness and ability to learn can matter more. Ask your quality team which case you are in.

How is a machine learning engineer different from a data scientist?

Titles overlap at many companies, so describe the work instead of relying on the label. Broadly, an ML engineer focuses on building and running models in production, while a data scientist focuses more on analysis and modeling. We will cover data scientists separately.

Can a machine learning search be kept confidential?

Yes. Geeqers runs confidential searches on request, so your current team and competitors do not need to know you are hiring.

What does it cost to use a recruiter for this hire?

Fee models and rates vary, and we cover them in our recruitment agency fees guide. With Geeqers the fee is agreed in writing before we start and is due only after your hire starts work.

Image: "Brunette woman looking at chemical formula on laptop screen" by Artem Podrez via Pexels, used under the Pexels licence. Survey and guidance details were checked against the sources linked above in October 2026.