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Remote Machine Learning Jobs in Berkeley, CA (NOW HIRING)

Del Oro Consulting

Software Engineer

San Francisco, CA · Remote

$55 - $58/hr

Familiarity with machine learning concepts and tools * Working knowledge of SQL and relational databases * Ability to work independently in a remote setting during PST hours Details of Position

Swish Analytics

Tennis Data Scientist

San Francisco, CA · On-site +1

$135K - $190K/yr

This position is remote from the USA. Duties: * Ideate, develop and improve machine learning and statistical models that drive Swish's core algorithms for producing state-of-the-art sports betting ...

6sense

Sr. Data Scientist

San Francisco, CA · Remote

$162K - $238K/yr

You will work on advanced machine learning, NLP, LLMs, Agentic AI, and GenAI applications that ... remote Notice of Collection and Use of Personal Information for California Residents: California ...

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All JobsRemote Machine Learning JobsRemote Machine Learning Jobs in Berkeley, CA

Remote Machine Learning information

See Berkeley, CA salary details

$31.2K

$52.1K

$107.8K

How much do remote machine learning jobs pay per year?

As of Jun 28, 2026, the average yearly pay for remote machine learning in Berkeley, CA is $52,141.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,800.00 and $56,300.00 per year, depending on experience, location, and employer.

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

To thrive as a Remote Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (often Python), and experience with machine learning frameworks, typically supported by a relevant degree. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (like AWS or GCP), and version control systems is crucial. Strong problem-solving abilities, self-management, and effective virtual communication distinguish top performers in remote settings. These competencies ensure the engineer can build effective models, collaborate across distributed teams, and deliver impactful solutions independently.

What Are Remote Machine Learning Jobs?

Machine learning is a method of analyzing data via automating analytical model building. The premise is that systems can learn from data. Machine learning positions include machine learning engineer, computer vision engineer, and senior deep learning engineer. In a remote machine learning job, you work from home in a branch of artificial intelligence performing duties related to computational processing and data. Your goal is to design models that solve business problems, such as helping organizations avoid unknown risks or find profitable opportunities. Your responsibilities include maintaining data pipelines, performing model research and implementation, building machine learning systems, and onboarding new utilities.

Can I work remotely as a machine learning engineer?

Yes, many machine learning engineer roles are available for remote work, especially in companies that support flexible or distributed teams. Remote positions often require strong skills in programming, data analysis, and familiarity with tools like Python, TensorFlow, or PyTorch, along with good communication skills. However, some roles may require on-site presence for collaboration or access to specialized hardware.

What is a remote machine learning job?

A remote machine learning job involves working with algorithms, data, and models to develop predictive systems or automate tasks, all while working from a location outside of a traditional office setting. Professionals in this role use techniques from statistics and computer science to analyze data, train machine learning models, and deploy solutions for real-world applications. Remote machine learning jobs can span various industries, including technology, healthcare, finance, and e-commerce. These roles typically require strong programming skills, knowledge of machine learning frameworks, and the ability to communicate findings effectively with team members or stakeholders. Working remotely offers flexibility, but also requires discipline and self-motivation to succeed.

Which 5 jobs will survive AI?

Remote machine learning roles such as data scientists, AI researchers, machine learning engineers, AI product managers, and AI ethics specialists are expected to persist as AI advances. These jobs require specialized skills in programming, statistical analysis, and domain expertise that are difficult to fully automate. Continuous learning and proficiency in tools like Python, TensorFlow, or PyTorch are essential for these roles.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in competitive markets.

Are ML jobs in demand?

Machine Learning (ML) jobs are in high demand across various industries such as technology, finance, healthcare, and retail. The growth is driven by increasing adoption of AI solutions, data-driven decision making, and the need for expertise in programming, data analysis, and model deployment, making ML a promising career path.

What are some effective strategies for collaborating with team members while working remotely as a Machine Learning Engineer?

Collaboration in a remote Machine Learning role often relies on clear communication through digital tools such as Slack, Zoom, and project management platforms like Jira or Asana. Regular check-ins and stand-up meetings help keep everyone aligned on project goals and timelines. Sharing code and models via version control systems (like Git) and using collaborative notebooks (such as JupyterHub or Google Colab) are also common practices. Building strong documentation habits and proactively seeking feedback can help ensure smooth teamwork and project success, even across different time zones.

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

AspectRemote Machine LearningData Scientist
Required CredentialsBachelor's/Master's in CS, ML certificationsBachelor's/Master's in CS, Statistics, or related field
Work EnvironmentRemote, collaborative teams, tech companiesRemote or on-site, diverse industries, analytics focus
Industry UsageTech, AI startups, researchFinance, healthcare, e-commerce, tech
Search & Comparison IntentOften compared for technical roles in AI/MLBroader data analysis roles, but overlapping skills

Remote Machine Learning specialists focus on developing algorithms and models primarily in tech environments, often requiring advanced programming and ML knowledge. Data Scientists analyze data to extract insights, sometimes utilizing ML techniques. While both roles share skills and credentials, Remote Machine Learning emphasizes model development, whereas Data Scientists focus on data analysis and interpretation.

More about Remote Machine Learning jobs
What are the most commonly searched types of Machine Learning jobs in Berkeley, CA? The most popular types of Machine Learning jobs in Berkeley, CA are:
What are popular job titles related to Remote Machine Learning jobs in Berkeley, CA? For Remote Machine Learning jobs in Berkeley, CA, the most frequently searched job titles are:
What job categories do people searching Remote Machine Learning jobs in Berkeley, CA look for? The top searched job categories for Remote Machine Learning jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Remote Machine Learning jobs? Cities near Berkeley, CA with the most Remote Machine Learning job openings:
Remote Machine Learning jobs near you
Sr. Staff Machine Learning Engineer, Content Ecosystem

Sr. Staff Machine Learning Engineer, Content Ecosystem

Pinterest

San Francisco, CA • On-site, Remote

Other

Posted 6 days ago

Job description

Pinterest works when the content ecosystem works: when people can reliably find ideas that feel inspiring, trustworthy, and actionable-and when the ecosystem continuously learns what to create, surface, and sustain next. In this Sr. Staff ML Engineer role, you'll be the technical lead shaping how Pinterest understands and improves its content as a living marketplace: a dynamic system with feedback loops between users, creators/publishers, distribution, and long-term business outcomes.

You will define a durable ML strategy that goes beyond "engagement metrics" to improve overall ecosystem health-identifying where we're underserving content, uncovering the attributes that make content succeed, and designing optimization approaches that balance relevance, quality, diversity, integrity, and monetization. The problems are inherently multi-objective and long-horizon: the best decisions today should strengthen the ecosystem tomorrow. If you're excited by high-leverage technical leadership, rigorous ML thinking, and marketplace-style dynamics at scale, this role offers a chance to directly shape Pinterest's moat and the experience millions of people come to for ideas they can act on.

What you'll do:

  • Set technical strategy and vision for ML systems that improve the end-to-end content ecosystem, including supply, distribution, and engagement/utility outcomes.
  • Partner with DS teams to develop a content ecosystem measurement framework to quantify content health and performance (e.g., content quality, freshness, diversity, coverage, creator/content sustainability, and user value), and align it with company/business goals.
  • Identify and close content gaps by building models and insights that answer: what content is missing, for whom, in which contexts, and why.
  • Deeply understand what content works and why by combining causal thinking, experimentation, and model interpretability to connect content attributes and distribution mechanisms to downstream user and business outcomes.
  • Build and optimize content marketplace mechanisms that balance multi-sided incentives and constraints (e.g., users, creators/publishers, advertisers, internal policy/safety), while maximizing long-term ecosystem value.
  • Design multi-objective optimization approaches that manage tradeoffs across relevance, quality, diversity, creator incentives, integrity/safety, and monetization.
  • Partner closely with cross-functional teams (Product, Data Science, UX Research, Content/Creator teams, Trust & Safety, Ads, Infra) to translate ambiguous ecosystem problems into clear technical roadmaps and deliver measurable impact.
  • Mentor and grow junior ML engineers through technical coaching, design reviews, career development support, and creating a culture of strong engineering and scientific rigor.
  • Raise the quality bar for ML engineering by establishing best practices for data quality, model governance, reliability, privacy-aware design, and operational excellence.
  • Communicate clearly and influence broadly by producing crisp technical proposals, aligning stakeholders on tradeoffs, and driving decisions across org boundaries.
  • Explore and apply advanced methods where beneficial-e.g., game-theoretic approaches, reinforcement learning, mechanism design, or bandit-style optimization-to improve marketplace dynamics and long-term ecosystem outcomes.

What we're looking for:

  • Strong fundamentals in machine learning and optimization, with the ability to apply them to real-world, high-scale ecosystem problems.
  • Demonstrated ability to lead technical strategy, navigate ambiguity, and deliver end-to-end impact.
  • Deep interest in marketplace dynamics (multi-sided incentives, feedback loops, long-term health metrics), and comfort with multi-objective tradeoffs.
  • Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
  • Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.
  • Not required but certainly a plus: background in game theory, reinforcement learning, mechanism design, or causal inference applied to ecosystems/marketplaces.
  • Bachelor's degree in computer science, machine learning, statistics, a related field or equivalent experience

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement: 

  • We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
  • This role will need to be in the office for in-person collaboration 1-2 times every 6 months and therefore can be situated anywhere in the country. 

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