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Remote Machine Learning Engineer Jobs in Los Altos, CA

OKX

Staff Machine Learning Engineer

San Jose, CA · Remote

About The Opportunity Building machine learning systems for risk at a global crypto exchange is fundamentally different from conventional ML engineering. The data spans on-chain activity, fiat ...

Tapestry

Senior Staff Machine Learning Engineer

Mountain View, CA · On-site +1

$144K - $190K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... and remote-sensing data, reinforcement learning for physical control systems, and multi-turn ... machine learning technical leads, Power Systems Scientists, Software Engineers, Product Managers ...

Unitytech

Senior Machine Learning Engineer

Mountain View, CA · On-site +1

$123K - $169K/yr

  • Medical

  • Life

  • Retirement

  • PTO

We're looking for a Senior Machine Learning Engineer to lead the development of these foundational AI systems within the Unity engine, empowering creators to build smarter, more responsive in-game ...

Otter.ai

Senior Machine Learning Engineer

Mountain View, CA · On-site +1

$230K - $265K/yr

As a Senior Machine Learning Engineer, you'll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systems-creating and transforming innovative ...

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All JobsMachine Learning Engineer JobsRemote Machine Learning Engineer JobsRemote Machine Learning Engineer Jobs in Los Altos, CA

Remote Machine Learning Engineer information

See Los Altos, CA salary details

$37.3K

$152.5K

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How much do remote machine learning engineer jobs pay per year?

As of Aug 14, 2026, the average yearly pay for remote machine learning engineer in Los Altos, CA is $152,502.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,200.00 and $183,600.00 per year, depending on experience, location, and employer.

What are some typical challenges faced by remote machine learning engineers, and how are they addressed?

Remote Machine Learning Engineers often face challenges such as coordinating across different time zones, ensuring smooth communication with team members, and accessing large datasets or secure environments remotely. Organizations commonly address these by using robust collaboration tools (like Slack, GitHub, and Jira), establishing clear documentation, and setting regular virtual meetings to maintain alignment. Many companies also provide secure remote environments or VPN access for handling sensitive data and code. Proactive communication and organized workflows help mitigate these challenges, enabling engineers to remain productive and connected to their teams.

What are the key skills and qualifications needed to thrive as a remote machine learning engineer?

To thrive as a Remote Machine Learning Engineer, you need a strong background in computer science, mathematics, and experience with machine learning algorithms, typically supported by a relevant degree and prior project work. Proficiency with programming languages like Python, machine learning frameworks such as TensorFlow or PyTorch, and familiarity with cloud computing platforms is crucial, and certifications like AWS Certified Machine Learning can enhance your profile. Excellent communication, self-motivation, and time-management skills are also essential for collaborating across remote teams and meeting project goals. These combined technical and soft skills are vital for developing effective machine learning solutions while ensuring productivity and collaboration in a virtual work environment.

What is a remote machine learning engineer?

A Remote Machine Learning Engineer designs, develops, and deploys machine learning models while working from a remote location. They preprocess data, train and optimize models, and integrate them into production systems. Their role often involves collaborating with data scientists, software engineers, and stakeholders to solve complex problems using AI. Strong programming skills in Python, experience with ML frameworks like TensorFlow or PyTorch, and cloud computing knowledge are essential. Remote ML engineers must also communicate effectively and manage their time efficiently to work asynchronously with teams.

What are the most commonly searched types of Machine Learning Engineer jobs in Los Altos, CA?

The most popular types of Machine Learning Engineer jobs in Los Altos, CA are:

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For Remote Machine Learning Engineer jobs in Los Altos, CA, the most frequently searched job titles are:

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Remote Machine Learning Engineer jobs near you
Infographic showing various Remote Machine Learning Engineer job openings in Los Altos, CA as of August 2026, with employment types broken down into 90% Full Time, and 10% Contract. Highlights an 100% Remote job distribution, with an average salary of $152,502 per year, or $73.3 per hour.

Staff Machine Learning Engineer

OKX

San Jose, CA • Remote

Full-time

Posted 29 days ago

Job description

Who We Are

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom.

OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. 

Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er.

OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more.

About The Opportunity
Building machine learning systems for risk at a global crypto exchange is fundamentally different from conventional ML engineering. The data spans on-chain activity, fiat deposits and withdrawals, trading behaviour, account access patterns, device intelligence, identity information, and customer interactions-signals that very few organizations can analyze together.
The problems are complex, adversarial, and constantly evolving. Models must identify emerging fraud patterns, scams, account takeovers, payment abuse, and other forms of financial risk while minimizing disruption to legitimate customers. Success is not measured only through offline model metrics. It is measured through prevented losses, improved approval rates, reduced false positives, faster investigations, and more reliable customer experiences.
This role sits within a multidisciplinary risk team of machine learning engineers, data scientists, risk strategy specialists, analytics engineers, product managers, and operations teams. You will work across the full ML lifecycle-from problem formulation, feature engineering, and model development to real-time deployment, monitoring, experimentation, and continuous iteration.
You will also help shape how AI is used across the risk organization. LLM-assisted development, automated model workflows, AI-powered investigations, and intelligent review agents are part of the team's daily work. We are looking for engineers who already use these tools effectively and can help establish safe, scalable, and production-ready AI practices.
 
What You'll Be Doing
  • Design, build, and deploy machine learning models for risk use cases such as payment fraud, account takeover, scam detection, deposit and withdrawal risk, promotional abuse, customer risk assessment, and transaction monitoring.
  • Own production ML systems end to end, including feature pipelines, training workflows, model serving, decision integrations, monitoring, alerting, drift detection, retraining, and incident response.
  • Partner with risk strategy and product teams to translate models into effective production controls, including approval, rejection, review, cooldown, limit adjustment, account restriction, and other risk mitigation actions.
  • Work closely with risk operations teams to understand investigation workflows, incorporate reviewer feedback, improve model explainability, and continuously refine labels and training data.
  • Apply AI-assisted development throughout the engineering workflow, using LLM coding tools to accelerate implementation, testing, debugging, analysis, and documentation while maintaining appropriate security and review standards.
  • Develop AI-powered risk capabilities such as investigation agents, case summarization, evidence collection, review recommendations, alert triage, suspicious-entity mining, and automated decision support.
  • Take research-stage models into reliable production systems by validating feature logic, reviewing data quality, addressing latency and scalability constraints, and ensuring consistency between offline training and online inference.
  • Ensure models and decision systems are explainable, traceable, and well documented so that model outputs can be understood by risk operations, product stakeholders, internal governance teams, and regulators where applicable.
  • Design, build, and deploy LLM-based agents for risk operations and investigation workflows, including case triage, evidence retrieval, transaction analysis, alert summarization, review recommendations, and automated action orchestration.
  • Develop production-grade agent architectures using tool calling, retrieval-augmented generation, workflow orchestration, structured outputs, memory, guardrails, and human-in-the-loop controls.
  • Build evaluation frameworks for LLM agents, measuring factual accuracy, task completion, decision consistency, latency, cost, reviewer acceptance, and operational impact. Ensure LLM agents operate safely in a regulated risk environment by implementing permission controls, audit logs, data privacy protections, prompt and tool security, fallback mechanisms, and clear escalation paths.
What We Look For in You
  • Significant professional experience in machine learning engineering, applied data science, or a closely related field, with a strong record of taking models from prototype to production. Scope and level will be calibrated based on experience.
  • Strong Python skills and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, XGBoost, LightGBM, or scikit-learn.
  • Strong knowledge of applied machine learning fundamentals, including supervised learning, anomaly detection, representation learning, class-imbalanced modeling, model calibration, and evaluation under changing data distributions.
  • Demonstrable fluency with AI-assisted engineering. You regularly use LLM coding tools, have built AI-integrated workflows or applications, and understand both the productivity benefits and the security, reliability, and governance risks.
  • Familiarity with model explainability techniques such as SHAP, feature attribution, reason-code generation, and model scorecards.
  • Hands-on experience designing and deploying production LLM agents, including agentic workflows, tool calling, retrieval-augmented generation, prompt and context management, structured output generation, and multi-step task orchestration.
  • Experience integrating LLM agents with internal systems, APIs, databases, search tools, case-management platforms, or decision engines to automate complex operational workflows.
  • A strong understanding of LLM-agent evaluation and reliability, including hallucination control, grounding, observability, permissions, failure handling, human review, latency, and cost optimization.
  • Experience building AI agents for fraud, risk, compliance, customer operations, cybersecurity, or other high-stakes domains is a meaningful advantage.
  • Strong communication and collaboration skills, with the ability to work effectively with engineers, data scientists, risk specialists, product managers, operations teams, and legal or compliance stakeholders.
OKX Statement
The base salary range for this position is $214,666 to $321,999. The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via Okcoin and OKX internal or external careers site.
 
OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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