Applied ML Scientist
Applied ML Scientists design and optimize machine learning systems that solve concrete business or scientific problems, moving beyond theoretical research to ship models in production environments. They work at the intersection of modeling and systems engineering, combining cutting-edge techniques like fine-tuning, reinforcement learning, and synthetic data generation with practical constraints around latency, cost, and real-world data distribution. These roles typically sit within dedicated applied research or product teams at AI-native companies, collaborating closely with engineers and domain experts to translate customer requirements or product challenges into effective training pipelines and evaluation frameworks.
Measured across 33 of 33 open postings.
This role is advertised at one level, so a single figure for the role would describe none of them. Experience and pay are the midpoints for each level on its own.
| Level | Share | Median years | Median pay |
|---|---|---|---|
| Senior | 39%(13) | — | — |
A dash means too few postings stated it to report a midpoint at any level. Most companies do not publish a salary band, so pay is indicative rather than a market rate. 4 levels with fewer than 10 open postings are not shown.
“working with cutting-edge LLMs, NLP, and machine learning to tackle our most ambiguous and complex customer problems”
“work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence”
“applying causal inference or machine learning, and building analytical systems”
“evaluate AI-assisted People systems and high-impact talent processes; evaluating machine-learning models, generative AI systems, agents”
Requirements are a share of every open posting, so a role missing from this list is one where almost nobody asks. Work mode is different: many postings never say, so that figure counts only the ones that do.
Skills
What companies are looking for in this role.
ML systems development
Research experiment design
Research-to-production translation
Model architecture design
Statistical modeling and uncertainty
Predictive modeling and forecasting
Post-training and alignment methods
Large-scale model training
Experimentation and causal inference
Research synthesis and insight
Training data curation
Data pipeline engineering
AI evaluation design
AI for scientific discovery
Agentic system research
Frontier AI research
LLM application development
Inference performance optimization
Computer vision and perception
Retrieval and ranking systems
AI-assisted development workflow
Research leadership and strategy
Research rigor and reproducibility
Mentoring and code review
Technology
The tools and technologies that define this role.
Open Jobs
33 open Applied ML Scientist jobs across 21 companies.
Other Research & Science roles
Scientists conducting original research to advance the state of the art in AI, machine learning, and related fields.
Engineers who build the systems, tools, and infrastructure that enable research.
Researchers and engineers doing core model development at AI labs: pre-training, mid- and post-training, reinforcement learning, training data, and model behaviour — the work most often titled "Member of Technical Staff" or "Researcher, Training".
Leaders who manage research teams, set research agendas, and guide scientific strategy.
Scientists working in chemistry, biology, physics, pharmacology, and other physical or life sciences.