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Remote Machine Learning Postdoc Jobs in Los Angeles, CA

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Remote Machine Learning Postdoc information

Is ML a high paying job?

Machine learning postdoctoral positions are generally well-paid compared to many academic roles, with salaries often ranging from $60,000 to over $100,000 annually depending on experience, location, and funding. These roles typically require strong programming skills in Python or R and knowledge of algorithms and data analysis, which can contribute to higher compensation levels.

Is a PhD in ML worth it?

A PhD in machine learning can enhance qualifications for a remote machine learning postdoc position, often leading to higher-level research opportunities and increased earning potential. However, it requires significant time investment and may not be necessary for industry roles that value practical skills and experience with tools like Python and TensorFlow. The decision depends on career goals and the specific requirements of the desired position.

What are the key skills and qualifications needed to thrive as a Remote Machine Learning Postdoc, and why are they important?

A Remote Machine Learning Postdoc requires a PhD in computer science, statistics, or a related field, with expertise in machine learning algorithms, statistical modeling, and research methodologies. Proficiency in programming languages like Python or R, experience with machine learning frameworks such as TensorFlow or PyTorch, and familiarity with version control systems (e.g., Git) are typically necessary. Strong written and verbal communication, self-motivation, and collaboration skills are vital for remote research and effective teamwork. These capabilities enable impactful independent research, smooth collaboration across distributed teams, and the successful dissemination of findings to the wider scientific community.

Is a postdoc harder than a PhD?

A remote machine learning postdoc typically involves more specialized research, higher expectations for independence, and often requires advanced skills in programming and data analysis. While a PhD focuses on completing a dissertation and gaining foundational expertise, a postdoc emphasizes producing publishable research and may involve longer hours and greater responsibility, making it generally more demanding in terms of research output and expertise. However, the difficulty varies based on individual experience and research environment.

What is a Remote Machine Learning Postdoc?

A Remote Machine Learning Postdoc is a postdoctoral researcher specializing in machine learning who works predominantly or entirely from a location outside their host institution, often from home. Their work involves conducting advanced research, developing new algorithms, analyzing data, and publishing findings related to machine learning while collaborating virtually with faculty and research teams. This role is ideal for researchers seeking flexibility or those who cannot relocate but wish to contribute to academic or industrial research from a distance.

Do you need H-1B for postdoc?

A remote machine learning postdoctoral position typically does not require H-1B sponsorship if the candidate is already authorized to work in the country, such as through a visa or citizenship. However, international candidates may need H-1B or other work visas depending on the employer and local immigration laws. Employers often sponsor visas for postdocs to comply with legal requirements and facilitate employment.

What are some common challenges faced by remote machine learning postdocs when collaborating with research teams?

Remote machine learning postdocs often encounter challenges related to communication and coordination, especially when working across different time zones or with teams that have varying schedules. Effective collaboration usually requires proactive communication through virtual meetings, shared code repositories, and regular progress updates. Building rapport with colleagues and staying engaged with ongoing research discussions can take extra effort remotely, but leveraging collaborative tools and participating in virtual seminars or group chats can help bridge the gap. Being organized and self-motivated is key to ensuring productive contributions to the team’s research objectives.
What are the most commonly searched types of Machine Learning Postdoc jobs in Los Angeles, CA? The most popular types of Machine Learning Postdoc jobs in Los Angeles, CA are:
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Remote Machine Learning Postdoc jobs near you

Rainmaker Fellow, Machine Learning

Rainmaker Technology Corporation

El Segundo, CA • On-site, Remote

Full-time

Medical, Dental, Vision

Posted 5 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.

Rainmaker is accepting expressions of interest while aggressively building its dedicated ML capability. Applications may be reviewed before a specific project and start date are finalized. A fellowship will begin only after the fellow is matched with a ready project, usable data, and a credible hands-on ML mentor.

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
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