ZipRecruiter

Log In

1

Postdoctoral Machine Learning Jobs in Los Angeles, CA

next page

Showing results 1-20

All JobsPostdoctoral Machine Learning JobsPostdoctoral Machine Learning Jobs in Los Angeles, CA

Postdoctoral Machine Learning information

See Los Angeles, CA salary details

$26.9K

$63.6K

$90K

How much do postdoctoral machine learning jobs pay per year?

As of Sep 1, 2026, the average yearly pay for postdoctoral machine learning in Los Angeles, CA is $63,597.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,800.00 and $71,700.00 per year, depending on experience, location, and employer.

What is a postdoctoral machine learning?

A Postdoctoral Machine Learning job is a research-focused position for individuals who have recently earned a Ph.D. in machine learning, artificial intelligence, or a related field. It typically involves conducting advanced research, publishing papers, collaborating with academic or industry partners, and developing novel algorithms or models. These roles are often hosted by universities, research institutes, or tech companies. The position helps researchers gain additional expertise and contribute to cutting-edge advancements before transitioning to faculty, industry, or independent research roles.

What are the typical daily responsibilities of a postdoctoral machine learning researcher?

A Postdoctoral Machine Learning researcher typically spends their day designing and implementing machine learning algorithms, analyzing experimental results, and preparing manuscripts for publication. They often collaborate with interdisciplinary teams of scientists and engineers, attend lab meetings, and contribute to grant writing or project proposals. Regular activities also include keeping up with recent scientific literature, mentoring graduate or undergraduate students, and presenting research findings at conferences or seminars. The blend of technical development and scientific communication makes each day dynamic and offers opportunities to influence both academia and industry.

What are the key skills and qualifications needed to thrive in a postdoctoral machine learning position?

To thrive as a Postdoctoral Machine Learning researcher, you need a strong background in machine learning theory, statistical analysis, and programming, typically supported by a Ph.D. in computer science, engineering, or a related quantitative field. Experience with Python, TensorFlow, PyTorch, and advanced data analytics tools is highly valued, as are relevant publications and experience with version control systems like Git. Strong problem-solving abilities, clear communication skills, and effective teamwork are crucial to excel in collaborative research settings. These skills and qualities are essential to drive innovative research, efficiently navigate complex datasets, and contribute to impactful scientific discoveries.

What are popular job titles related to Postdoctoral Machine Learning jobs in Los Angeles, CA?

For Postdoctoral Machine Learning jobs in Los Angeles, CA, the most frequently searched job titles are:

What job categories do people searching Postdoctoral Machine Learning jobs in Los Angeles, CA look for?

The top searched job categories for Postdoctoral Machine Learning jobs in Los Angeles, CA are:

What cities near Los Angeles, CA are hiring for Postdoctoral Machine Learning jobs?

Cities near Los Angeles, CA with the most Postdoctoral Machine Learning job openings:

Postdoctoral Machine Learning jobs near you
Infographic showing various Postdoctoral Machine Learning job openings in Los Angeles, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $63,597 per year, or $30.6 per hour.

Rainmaker Fellow, Machine Learning

Rainmaker Technology Corporation

El Segundo, CA • On-site

Full-time

Medical, Dental, Vision

Re-posted 9 days ago

Job description

About Rainmaker
Rainmaker is pioneering a modern cloud-seeding system to increase precipitation, improve water availability, and address severe-weather challenges. We combine atmospheric science, weather-resistant UAS, radar and satellite observations, numerical weather prediction, novel sensing systems, and sustainable seeding technologies to design, operate, and evaluate precipitation-enhancement programs.
Rainmaker collects unusual atmospheric datasets because we build sensors, operate aircraft, fly into clouds, and deliberately intervene in atmospheric systems. Our long-term advantage depends on turning those observations into better estimates, forecasts, and operational decisions.
About the Fellowship
The Rainmaker Machine Learning Fellowship is a paid, full-time research appointment for exceptional undergraduate and graduate students, postdoctoral researchers, recent graduates, and other early-career researchers.
As a fellow, you will join Rainmaker's R&D team and work alongside our researchers on a scoped machine-learning project drawn from Rainmaker's current research priorities and defined in close collaboration with your research lead or mentor. Project matching will consider available data, mentor capacity, team needs, and your background. You will take responsibility for a concrete workstream while contributing to the broader team's research, reviews, and technical decisions.
You will work with real sensor and operational data, establish credible baselines, build and evaluate models, and leave behind a durable dataset, system, or research artifact that Rainmaker can continue using. Fellows are not expected to arrive with an independent research agenda or define a project in isolation.
Examples of the Work
Fellowship projects change with Rainmaker's research and operational priorities. Examples of the work our ML team may pursue include:
  • Developing a short-range supercooled liquid water opportunity forecast using public NWP and Rainmaker observations.
  • Predicting hail-core growth, motion, splitting, and decay from radar sequences.
  • Building a bounded multimodal atmospheric-state reconstruction pilot.
  • Improving microwave-sounder retrievals using Rainmaker observations.
  • Modeling another scientific or operational problem selected with Rainmaker's ML and atmospheric-science teams.

What You'll Do
  • Translate a scientific or operational question into a measurable ML problem.
  • Build or improve the training and validation dataset needed for the project.
  • Establish simple, reproducible baselines before introducing more complex models.
  • Train, evaluate, and debug models using held-out weather events, regions, or operating conditions.
  • Quantify calibration, uncertainty, generalization, failure modes, and sensitivity to missing or biased data.
  • Work closely with atmospheric scientists to define useful targets, ground truth, physical constraints, and operational success criteria.
  • Produce clear, reusable code and documentation.
  • Present your results to Rainmaker's scientists, engineers, operators, and technical leadership.
  • Deliver a final artifact such as a benchmark dataset, model, prototype product, evaluation report, or research paper.

What We're Looking For
  • Current undergraduate, master's, or PhD students; postdoctoral researchers; recent graduates; and other early-career researchers are all eligible.
  • Strong Python programming ability and experience with a modern ML framework.
  • Evidence that you can independently build, test, and debug technical work.
  • Strong quantitative reasoning and an ability to design credible experiments.
  • Interest in noisy, sparse, multimodal, spatial, temporal, or physical data.
  • Ability to make progress on ambiguous research problems while incorporating mentor feedback.
  • Clear written and verbal communication.
  • Availability for full-time, on-site work in El Segundo for the agreed appointment.

Particularly Relevant Backgrounds
  • Machine learning, computer science, applied mathematics, statistics, physics, meteorology, remote sensing, robotics, autonomy, geospatial analysis, or scientific computing.
  • Forecasting, sequence modeling, computer vision, state estimation, sensor fusion, probabilistic modeling, data assimilation, or uncertainty quantification.
  • Weather knowledge is valuable but not required.

What Success Looks Like
By the end of the fellowship, you will have answered a clearly defined technical question and produced a rigorous, reusable result that advances the team's work. Depending on the project, that might be a benchmark dataset, evaluated model, prototype product, forecasting or retrieval improvement, or a well-supported analysis of performance and failure modes.
Success does not require a positive scientific result. A well-supported finding that the available data cannot answer the question-and a concrete recommendation for what Rainmaker should measure next-can be highly valuable.
Fellowship Details
  • Paid, full-time, and on-site in El Segundo.
  • Three-to-six-month appointment, with four months as the standard duration.
  • Rolling applications and flexible start dates based on project and mentor readiness.
  • Possible consideration for future full-time roles, without any promise or expectation of conversion.

Compensation and Benefits
$8,000 per month
Benefits:
  • Full health coverage (medical, dental, and vision insurance)
  • Lunch provided when working in-office and a fully stocked kitchenette
  • Free EV charging at the HQ

$8,000 - $8,000 a month

Apply