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Freelance Remote Risk Adjustment Coder Jobs in Santa Clara, CA

OKX

Staff Machine Learning Engineer

San Jose, CA

... adjustment, account restriction, and other risk mitigation actions. * Work closely with risk ... Apply AI-assisted development throughout the engineering workflow, using LLM coding tools to ...

Axiad

AI-First SRE/DevOps Engineer

San Jose, CA · On-site +1

$66.75 - $88.75/hr

Mesh translates identity exposure into financial impact, prioritizes risk across human, machine ... US (Remote or HQ Hybrid) Job Type: Full-time Axiad is seeking a skilled AI-First SRE/DevOps ...

Kier & Wright

Senior Project Land Surveyor

Santa Clara, CA · Remote

$104K - $150K/yr

... risk surveying assignments. The Senior Project Surveyor supports multiple project teams by ... Short and long plats, subdivision maps, and lot line adjustments * Easement exhibits and legal ...

Basin Enterprises Inc.

Be Seen First

Transmission & Distribution Line Estimator - Remote

San Jose, CA · Remote

$140K - $190K/yr

Apply prevailing wage assessments and adjustments as applicable according to State, Federal, IRA ... Assess and document cash flow, risk management, contingency, and margin strategies * Present ...

Basin Enterprises Inc.

Be Seen First

Transmission & Distribution Line Estimator - Remote

Milpitas, CA · Remote

$140K - $190K/yr

Apply prevailing wage assessments and adjustments as applicable according to State, Federal, IRA ... Assess and document cash flow, risk management, contingency, and margin strategies * Present ...

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Freelance Remote Risk Adjustment Coder information

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How much do freelance remote risk adjustment coder jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for freelance remote risk adjustment coder in Santa Clara, CA is $26.33, according to ZipRecruiter salary data. Most workers in this role earn between $21.15 and $28.22 per hour, depending on experience, location, and employer.

What is a freelance remote risk adjustment coder?

Freelance Remote Risk Adjustment Coders are healthcare professionals who work independently from various locations to review medical records and assign codes that reflect patients’ health conditions and treatments, focusing on risk adjustment models. Their primary role is to ensure accuracy in coding so that healthcare organizations receive appropriate reimbursement and maintain compliance with regulatory standards. These coders typically work on a contract basis, using secure digital platforms to access records and submit their coding work. They must be highly knowledgeable in ICD-10-CM coding guidelines, risk adjustment methodologies (such as HCC), and HIPAA regulations.

What are the key skills and qualifications needed to thrive as a freelance remote risk adjustment coder?

Thriving as a Freelance Remote Risk Adjustment Coder requires deep knowledge of medical coding (especially ICD-10-CM), risk adjustment models, and compliance standards, typically verified by certifications like CRC, CPC, or CCS. Proficiency with coding software, EHR systems, and secure remote work platforms is essential for accurate and efficient coding. Strong attention to detail, self-motivation, and reliable communication are vital soft skills for managing independent workloads and collaborating with clients remotely. These abilities ensure accurate risk score calculations, regulatory compliance, and successful client relationships in a virtual work environment.

How do freelance remote risk adjustment coders typically manage communication and workflow with healthcare clients and team members?

Freelance Remote Risk Adjustment Coders commonly use secure online platforms and project management tools to receive assignments, submit coded charts, and communicate with healthcare providers or project managers. Maintaining clear and prompt communication via email or dedicated messaging systems is crucial to clarify documentation, resolve coding queries, and ensure deadlines are met. Coders must be proactive in scheduling regular check-ins and staying updated on client-specific guidelines, as workflows can be fast-paced and require strong organizational skills. Collaboration often involves working independently but also participating in virtual meetings or training sessions to stay aligned with team quality standards.

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Freelance Remote Risk Adjustment Coder jobs near you
Infographic showing various Freelance Remote Risk Adjustment Coder job openings in Santa Clara, CA as of June 2026, with employment types broken down into 2% As Needed, 89% Full Time, and 9% Part Time. Highlights an 42% Physical, 2% Hybrid, and 56% Remote job distribution, with an average salary of $54,773 per year, or $26.3 per hour.

Staff Machine Learning Engineer

OKX

San Jose, CA

Full-time

Re-posted 4 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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