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Amazon Machine Learning Scientist Jobs (NOW HIRING)

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88K - $121K/yr

Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences) Hours: Full-Time, Salaried Location: Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote ...

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site

$88K - $121K/yr

Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences) Hours: Full-Time, Salaried Location: Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote ...

Amgen

Sr Machine Learning Scientist

Thousand Oaks, CA

$96K - $131K/yr

Machine Learning Scientist What you will do Let'sdo this.Let'schange the world.Within Amgen's Research and Development organization, the Therapeutic Protein Design (TPD) team supports the broad goal ...

Axon

Senior Machine Learning Scientist

Seattle, WA · On-site

$104K - $142K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI team, focusing on AI applications (LLM and Computer Vision) in Cloud, Devices and Robotics. As a ...

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99K - $135K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI team, focusing on AI applications (LLM and Computer Vision) in Cloud, Devices and Robotics. As a ...

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88K - $121K/yr

Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences) Hours: Full-Time, Salaried Location: Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote ...

DISCO

Senior Machine Learning Scientist

Austin, TX · On-site

$97K - $124K/yr

Your Impact The Senior Machine Learning Research Scientist is a key contributor to DISCO's machine learning and AI research initiatives, leading the development of advanced algorithms and ...

DISCO

Senior Machine Learning Scientist

Austin, TX · On-site

$97K - $124K/yr

They are seeking a Senior Machine Learning Research Scientist to lead the development of advanced algorithms and methodologies, manage research programs, and mentor junior researchers in their ...

Axon

Senior Machine Learning Scientist

Seattle, WA · On-site

$104K - $142K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI team, focusing on AI applications (LLM and Computer Vision) in Cloud, Devices and Robotics. As a ...

Axon

Senior Machine Learning Scientist

Scottsdale, AZ · On-site

$92K - $125K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI team, focusing on AI applications (LLM and Computer Vision) in Cloud, Devices and Robotics. As a ...

DISCO

Senior Machine Learning Scientist

Austin, TX · On-site

$97K - $124K/yr

They are seeking a Senior Machine Learning Research Scientist to lead the development of advanced algorithms and methodologies, mentor junior researchers, and manage complex research initiatives.

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Amazon Machine Learning Scientist information

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$24.5K

$134.5K

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How much do amazon machine learning scientist jobs pay per year?

As of Jun 7, 2026, the average yearly pay for amazon machine learning scientist in the United States is $134,515.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $162,000.00 per year, depending on experience, location, and employer.

What is the difference between Amazon Machine Learning Scientist vs Data Scientist?

AspectAmazon Machine Learning ScientistData Scientist
Required CredentialsMaster's or PhD in Computer Science, Data Science, or related fields; experience with machine learning frameworksDegree in Data Science, Statistics, Computer Science, or related fields; strong analytical skills
Work EnvironmentFocus on developing and deploying machine learning models within cloud platforms like AWSAnalyze large datasets, create reports, and develop insights across various industries
Employer & Industry UsagePrimarily in tech companies, cloud service providers, and AI-focused organizationsWidely used across finance, healthcare, marketing, and tech sectors

Amazon Machine Learning Scientists specialize in designing and implementing machine learning models within AWS, requiring advanced technical credentials. Data Scientists analyze data to generate insights across industries. While both roles require strong analytical skills, Machine Learning Scientists focus more on model development and deployment, whereas Data Scientists emphasize data analysis and reporting.

More about Amazon Machine Learning Scientist jobs
What job categories do people searching Amazon Machine Learning Scientist jobs look for? The top searched job categories for Amazon Machine Learning Scientist jobs are:
Amazon Machine Learning Scientist jobs near you
Infographic showing various Amazon Machine Learning Scientist job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $134,515 per year, or $64.7 per hour.

Senior Machine Learning Scientist

Zanskar

Salt Lake City, UT • On-site, Remote

$88K - $121K/yr

Full-time

Posted 16 days ago

Job description

Role Overview 
Title: Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences)
Hours: Full-Time, Salaried
Location: Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote)
Benefits Eligible: Yes
Manager: Head of Reservoir R&D
 
Why we exist
Geothermal energy is the most abundant renewable energy source in the world. There is 2,300 times more energy in geothermal heat in the ground than in oil, gas, coal, and methane combined. However, historically it’s been hard to find and expensive to develop. At Zanskar, we’re building technology to find and develop new geothermal resources in order to make geothermal a cheap and vital contributor to a carbon-free electrical grid.
 
To do that, we combine deep subsurface expertise with advanced AI technologies—including modern machine learning, scalable scientific computing, and uncertainty-aware modeling—to dramatically improve geothermal discovery and development outcomes. We build systems that can learn from sparse and noisy data, emulate expensive physics simulations, and help teams make faster, higher-confidence decisions about where to drill and how to develop fields.
 
Who you are
You will help build the modeling and decision-making core of Zanskar’s geothermal exploration software. This role blends scientific machine learning (surrogate modeling) with sequential decision-making under uncertainty. A successful candidate will:
Explore: you’re open-minded about methods and will prototype, benchmark, and iterate across approaches.
Reproduce & adapt: you can implement ideas from papers and new frameworks quickly, then harden the best ones into reliable workflows.
Decision-minded: you care about end-to-end outcomes (value, risk, time-to-decision), not just model accuracy.
Uncertainty-first: you build models that are accurate, well-calibrated, and dependable under distribution shift and sparse data regimes.
Collaborative: you work well with domain experts and can translate between geology/engineering intuition and ML systems.
 
What you’ll do
Build fast, reliable models that emulate or augment computationally expensive physics-based simulations (e.g., reservoir, wellbore, and coupled multi-physics workflows).
Evaluate and compare multiple modeling approaches (physics-informed, operator learning, transformers, diffusion models, etc.), establishing strong baselines and selecting methods based on evidence.
Build multi-step decision systems for exploration and appraisal: POMDP-style planning and belief-space decision making to recommend exploration steps.
Translate scientific and engineering questions into well-defined learning and decision problems: inputs/outputs, constraints, boundary/initial conditions, reward/cost structure, and success metrics (e.g., expected NPV, probability of success, downside risk).
Prototype, benchmark, and iterate across approaches (POMDP solvers, RL methods, VOI-style baselines, MPC-style replanning), then harden the best ones into reliable workflows and APIs.
Collaborate deeply with geoscientists, reservoir engineers, and software engineers to integrate these models and policies into production software.
 
What we’re looking for
3+ years of applied ML experience, ideally in scientific ML, decision-making under uncertainty, surrogate modeling, robotics/control, or related engineering/science domains.
Expertise in python and modern ML tooling (PyTorch preferred).
Track record of taking models from prototype → rigorous evaluation → adoption by technical stakeholders.
Strong fundamentals in probability/statistics and comfort with messy, real-world scientific datasets.
Experience building or using surrogate models for expensive simulators (PDE-driven systems, multi-physics, or similar).
Relevant technical strengths
Surrogate modeling. 
Sequential decision-making under uncertainty and reinforcement learning. 
Software engineering: Git, code review, reproducibility, CI basics, Docker/container workflows.
Experience with diffusion models.
Exposure to subsurface modeling domains: geothermal, oil & gas, CCS, hydrogeology, geoscience, or related.
Familiarity with cloud infrastructure and data systems (SQL, object storage, orchestration).
 
Location and Benefits
This position is based out of our headquarters in Salt Lake City, Utah, and is hybrid.
Benefits include:
Paid holidays
15 days PTO + PTO accrual increase based on tenure
Medical, dental and vision coverage
401k 
Stock options
Growth opportunities at a company with a direct impact in displacing carbon emissions
Equal Opportunity Employer 
 
Zanskar is an equal-opportunity employer and complies with all applicable federal, state, and local fair employment practice laws.