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Director Applied Science Jobs (NOW HIRING)

Wizard

AI Applied Scientist

$225K - $280K/yr

... Applied Science (PhD or equivalent depth strongly preferred) * Hands-on experience evaluating modern AI/ML systems: LLMs, agents, ranking, or recommendations * Direct experience with LLM-based ...

Amazon

Director of Science, Geospatial

Bellevue, WA · On-site

The Director, Applied Science (Geospatial) owns the end-to-end science portfolio that enables these capabilities by leveraging innovative AI and ML techniques. They are responsible for (1) learning ...

Amazon

Director of Science, Geospatial

Bellevue, WA · On-site

The Director, Applied Science (Geospatial) owns the end-to-end science portfolio that enables these capabilities by leveraging innovative AI and ML techniques. They are responsible for (1) learning ...

Amazon

Director of Science, Geospatial

Bellevue, WA

The Director, Applied Science (Geospatial) owns the end-to-end science portfolio that enables these capabilities by leveraging innovative AI and ML techniques. They are responsible for (1) learning ...

Clipboard Health

Applied Scientist

San Francisco, CA · On-site

$160K - $225K/yr

About the Role We're building a new team, Applied Science, and we're looking for our first outside ... Range reflects experience; we'll be direct about where you'd land. Work Location This will be a ...

Amazon

Sr. Applied Science , AWS Agentic AI

Santa Clara, CA · On-site

$143K - $189K/yr

Our customers bring problems that will give Applied Scientists like you endless opportunities to ... Criminal history may have a direct, adverse, and negative relationship with some of the material ...

Amazon

Sr. Applied Science , AWS Agentic AI

Santa Clara, CA · On-site

$143K - $189K/yr

Our customers bring problems that will give Applied Scientists like you endless opportunities to ... Criminal history may have a direct, adverse, and negative relationship with some of the material ...

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Director Applied Science information

What does a Director of Applied Science do?

A Director of Applied Science leads teams of scientists and researchers to develop and implement practical solutions using scientific methods and data analysis. They oversee projects that apply advanced research to solve real-world business problems, often working in fields like machine learning, artificial intelligence, or data science. This role involves strategic planning, team management, and collaboration with other departments to ensure scientific initiatives align with organizational goals. Directors of Applied Science are also responsible for mentoring staff, setting research priorities, and communicating findings to stakeholders.

What are the key skills and qualifications needed to thrive as a Director of Applied Science, and why are they important?

To thrive as a Director of Applied Science, you need a deep expertise in scientific research, data analysis, and machine learning, typically supported by a PhD or advanced degree in a relevant field. Familiarity with programming languages (such as Python or R), cloud-based analytics platforms, and experience managing complex research projects are essential. Outstanding leadership, communication, and strategic vision help you guide teams and collaborate across departments. These skills ensure the effective development and implementation of innovative scientific solutions that drive business value.

How does a Director of Applied Science typically collaborate with cross-functional teams to drive innovation?

As a Director of Applied Science, you will frequently work alongside product managers, engineers, and data scientists to translate business objectives into actionable research and technical solutions. This collaborative environment requires strong communication skills to align on project goals, share findings, and integrate scientific advancements into product development. Regular meetings, joint planning sessions, and cross-team workshops are common practices, ensuring that scientific insights directly inform product strategy and drive innovation. Building strong working relationships across departments is key to maximizing the impact of applied science within the organization.
What are the most commonly searched types of Applied Science jobs? The most popular types of Applied Science jobs are:
Infographic showing various Director Applied Science job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 85% Full Time, 13% Part Time, and 1% Temporary. Highlights an 90% Physical, 3% Hybrid, and 7% Remote job distribution.
AI Applied Scientist

AI Applied Scientist

Wizard

Remote

$225K - $280K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 17 days ago

Job description

About Wizard
Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.
The Role
We're looking for an Applied Scientist to own how we measure, understand, and improve the accuracy of our AI agent. This role sits at the intersection of applied ML, evaluation science, and product. You'll define what "good" looks like for our agent, build the systems to measure it, and lead the science work to improve it, including fine-tuning the LLM judges that power our evaluation pipeline.
You'll partner with ML Engineering and AI Engineering. What you will do is bring scientific rigor to the most important question at Wizard: is our agent getting better, and how do we know?
This is a foundational hire on our science team. Evaluation is the starting point, and the role is scoped to grow into broader applied science work as the surface area of the agent expands (recommendations, personalization, ranking, multimodal, conversational understanding).
What You'll Do
  • Define and evolve accuracy metrics across the full shopping experience (retrieval, ranking, recommendations, outcomes)
  • Design and run experiments to measure improvements and regressions
  • Build and maintain evaluation datasets, benchmarks, and scoring frameworks
  • Improve the LLM judges that power our evaluation pipeline: prompting, calibration, and fine-tuning where it matters
  • Translate ambiguous product questions into clear, measurable hypotheses and analysis
  • Partner with ML Engineers to validate model changes and guide iteration
  • Identify failure modes and edge cases, and drive improvements through data
  • Make agent performance visible, trusted, and actionable across product and engineering
First 3 months
  • Go deep on the agent, the current eval pipeline, and the metrics we use today
  • Audit existing accuracy metrics and benchmarks; identify gaps, blind spots, and signals that aren't trustworthy
  • Build relationships with ML, AI Engineering, and Product
  • Ship one quick win: a missing benchmark, an improved metric, or a fix to a misleading signal
  • Establish a baseline view of agent performance the team can rally around
Months 3 to 6
  • Own the evaluation framework: datasets, metrics, scoring, reporting, both offline and online
  • Drive measurable improvements to LLM judge quality (calibration, fine-tuning where appropriate)
  • Run experiments that influence at least one significant model or product change
  • Stand up automated evaluation the team trusts before and after every launch
  • Build dashboards and reporting that make agent performance legible to leadership
Beyond 6 months
  • Lead applied science work on the next frontier as the agent grows: multi-turn evaluation, multimodal, personalization, ranking quality, conversational understanding
  • Influence team-level strategy on what we measure, what we improve, and why
  • Mentor and help grow the science function as it expands
What Success Looks Like
  • Clear, trusted accuracy metrics are consistently used across product and engineering
  • A robust automated evaluation framework for both offline and live experiments
  • Model and product changes are consistently measured before and after launch
  • Demonstrable improvements in LLM judge quality and eval coverage
  • Science leadership that informs what we build, not just whether it works
Career Growth
  • Depth track: become the org's authority on AI evaluation: eval strategy, judge models, agent benchmarking
  • Breadth track: expand into other applied science problems (recommendations, personalization, ranking, multimodal, conversational understanding) as those areas come online
  • Leadership track: Senior / Staff Applied Scientist, with technical leadership across the science function
  • As the agent gets more capable, the science problems get richer
Ideal Background
  • 5+ years in Applied ML, AI Research, or Applied Science (PhD or equivalent depth strongly preferred)
  • Hands-on experience evaluating modern AI/ML systems: LLMs, agents, ranking, or recommendations
  • Direct experience with LLM-based systems: judge models, RAG, prompt engineering, fine-tuning, RLHF, or similar
  • Strong experimentation foundations: A/B testing, causal inference, statistical rigor
  • Proven ability to operate in ambiguity: defining problems, not just solving pre-defined ones
  • Clear, structured communication that influences across ML, engineering, and product
Compensation & Benefits
The expected base salary range for this role is $225,000 - $280,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.
In addition to base salary, Wizard offers:
  • Equity in the form of stock options
  • Medical, dental, and vision coverage
  • 401(k) plan
  • Flexible PTO and company holidays
  • Fully remote work within the United States
  • Periodic company offsites and team gatherings

Wizard is committed to fair, transparent, and competitive compensation practices.

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