Interested in this AI/ML Engineer role at Deep Science Ventures?
Apply Now →About This Role
Drug discovery is broken. We're building the AI that fixes it; a scalable closed\-loop agentic system that does the scientific reasoning and analysis end\-to\-end, generating traceable and verifiable predictions and decisions.
We're looking for an experienced founder and operator to join our Technical and Product founders. As Co\-founder \& CEO your job is to take a built, already\-used platform from spin\-out through a competitive seed round, a Series A and beyond.
The role is full\-time, UK\-based, spinning out of Deep Science Ventures over the coming months.
THE OPPORTUNITY
Drug discovery is one of the most important problems in the world, and one of the least solved. Biology is complex. The useful data is sparse. The work runs in slow cycles of trial and error. Teams spend years and billions of dollars on development, and most of the time it still fails.
We don't think the fix is more of the same. Brute force hasn't worked, throwing more compute and more data at biology hasn't made it predictable. Making today's process a little faster won't work either, not at any real scale, because it would still be capped by the scale of what human team can hold in their heads. What will work is a different way of doing the science itself: an AI framework that reasons through it end to end, drawing on everything, the literature, data and the wet\-lab, built to get the most out of what today's agentic models are great at while guarding hard against where they fail.
The proof\-of\-concept is done and has been tested on multiple use cases. In a single run it processed an entire disease's single\-cell data, ingested hundreds of experimental facts extracted from literature, built a connected map of the biology, and returned drug\-target hypotheses. Each of those was marked supported or refuted, and is traceable with provenance to the exact evidence behind it. When the data is this thin, that grounding is what separates an answer you can build on from one you take on faith. The work came out of a collaboration with the Allen Institute, and the first results are submitted to a top\-tier journal, pre\-print can be found here: https://www.biorxiv.org/content/10\.64898/2026\.07\.01\.734821v1\.
The current system already produces reliably strong results for certain parts of the drug development pipeline, such as target validation, powered by the data modalities we've built in. The plan is to scale that to every modality to reach what the whole field is chasing: reliably predicting what a clinical trial will do before it runs, with a concrete explanation of the biology of why.
Our approach is to prove things with hard, scored benchmarks rather than decks; decontaminated and point\-in\-time, so nothing leaks. We've built one for freedom\-to\-operate already, and the one that matters most is clinical\-trial prediction and mechanistic explanation, because a trial is the one place biology gives a straight answer. An engine validated on such a benchmark is more than capable of tackling every stage of drug development, and in time, not just predict an outcome but show how to change it.
That's the mission: not to make today's process a bit faster, but to change what's possible.
THE ROLE
This is a deeply hands\-on role. As Co\-founder \& CEO you own the commercial and company\-building side from day one, working hand\-in\-hand with the other cofounders, who own the engine and the science. You will:
- Shape strategy and positioning alongside the founding team.
- Lead fundraising: run a competitive seed round and the path to Series A.
- Build the commercial engine: partnerships, pilots and paying customers across pharma and biotech.
- Build the company: hiring, story, board, grant and academic partnerships, and day\-to\-day operations.
Location: Hybrid, UK\-based · Commitment: Full\-time from spin\-out · Equity: Meaningful co\-founder equity, milestone\-linked.
Requirements
We're after a rare kind of operator, someone with a real depth in their field. Maybe that depth is in AI, or AI for biology, and you've built innovative systems the field trusts, with a name that opens doors closed to others. Maybe it's in the clinic, and you've carried drug programs from lead optimisation through IND\-enabling studies, owning the go/no\-go calls as assets moved toward first\-in\-human. What stays constant is the caliber: you hold your own in a room of scientists and a room of investors, you lead from the front, and you're here to change an industry, not optimise a corner of it.
Essential Values
- Driven to build a category\-defining company at the frontier of AI and drug discovery, and to challenge how the industry works today.
- Impact\-driven: you take the initiative, make things happen, and think from first principles about what's really needed.
- Clear entrepreneurial spirit: with the ability to thrive in an ambiguous, unstructured, fast\-paced and demanding environment.
- Collaborative by nature: able to partner closely with both the product and tech cofounders.
Essential experience (must\-have)
- World\-class expertise in a related technical field (AI, ML, therapeutics, etc.).
- Either you've personally led a priced round, or your name and track record make one raisable.
- US or UK based (or flexible on moving) and able to go full\-time from spin\-out.
Preferred experience (nice\-to\-have)
- Experience in a leading AI lab or AI\-drug\-discovery company in a genuinely impactful role.
- A strong, transferable investor network in deep\-tech, AI, bio or health.
- Have scaled a company from seed to Series A and beyond.
- Commercial and business\-development track record: partnerships, pilots, paying customers.
- Experience selling technical or scientific software into pharma, biotech or research institutions.
Benefits
By joining DSV, you'll be joining a team of operators who have founded companies and led the translation of science at some of the most respected universities, charities, funds and government agencies. DSV is a leading deep\-tech venture studio with a portfolio of 50\+ science\-led companies at a total valuation of \~$700m.
- A genuine co\-founder equity stake, milestone\-linked so that delivering a strong round is richly rewarded.
- Access to proprietary venture\-building tools, resources and processes proven to create high\-impact companies from scratch.
- Access to DSV's global network of investors, advisors and industrial partners.
- A proven, working platform and a hard technical moat already in place \- your job is the market and the money, not the technology.
- Continuous post\-spinout support including fundraising, commercial partnerships, recruitment and team\-building.
ABOUT DSV
Deep Science Ventures (DSV) is on a mission to create a future in which both humans and the planet can thrive. We use our unique venture creation process to create, spin\-out, and invest in science companies, combining available scientific knowledge and founder\-type scientists into high\-impact ventures. Operating across Pharmaceuticals, Climate, Agriculture, and Computation, we tackle the challenges defining these areas by taking a first\-principles approach and partnering with leading institutions.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Deep Science Ventures, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Deep Science Ventures AI Hiring
Deep Science Ventures has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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