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Remote Bioinformatics Machine Learning Jobs in Washington

West 4th Strategy

Data Scientist (Remote)

Washington, DC · On-site +1

$135K - $150K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Build and implement machine learning models and/or predictive analytics. * Maintenance of ... Remote - Eastern Standard Time zone - preferred but not required CLEARANCE * U.S. Citizenship ...

BluePath Labs

AI Developer - Remote

Washington, DC · Remote

$110K - $160K/yr

  • Medical

  • Retirement

  • PTO

Remote (occasional travel may be required) Clearance:Public Trust or able to obtain Salary: $110 ... The AI Developer will design, build, and support artificial intelligence and machine learning ...

BluePath Labs

AI Developer - Remote

Washington, DC · On-site +1

$110K - $160K/yr

  • Medical

  • Retirement

  • PTO

Remote (occasional travel may be required) Clearance: Public Trust or able to obtain Salary: $110 ... The AI Developer will design, build, and support artificial intelligence and machine learning ...

West 4th Strategy

Data Scientist (Remote)

Washington, DC · Remote

  • Medical

  • Dental

  • Vision

  • Retirement

Build and implement machine learning models and/or predictive analytics. * Maintenance of ... Remote - Eastern Standard Time zone - preferred but not required CLEARANCE * U.S. Citizenship ...

BluePath Labs

AI Developer - Remote

Washington, DC · Remote

$110K - $160K/yr

  • Medical

  • Retirement

  • PTO

Remote (occasional travel may be required)Clearance:  Public Trust or able to obtain  Salary ... and machine learning solutions that address complex business and mission challenges. The role ...

Maximus

Data Scientist (Remote)

VA · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

General information Job Posting Title Data Scientist (Remote) Date Tuesday, August 4, 2026 City ... NLP, and machine learning (both supervised and unsupervised) to improve relevance and ...

ASSYST, Inc.

Software Developer

Sterling, VA · Remote

Develop prototypes and systems leveraging AI and Machine Learning for client projects and internal ... Remote work is not permitted. ASSYST Benefits: We are proud to offer a robust benefits package ...

Sparibis

Azure Data Architect

Washington, DC · On-site +1

$72.25 - $92.75/hr

Location: 100% Remote. This is a United States based position, and candidates must reside in the ... Expertise in statistical modeling, machine learning algorithms, and data mining techniques. * Must ...

Knexus

AI Software Engineer (Remote)

Vienna, VA · Remote

This is a fully remote position for candidates in the continental U.S., with work hours aligned to ... This role involves leveraging cutting-edge technologies, including GenAI and machine learning ...

YO AI Labs

Senior Software Engineer - Remote

Washington, DC · Remote

$138K - $182K/yr

Remote Job Summary: In this role, you'll apply your expertise to help train next-generation AI ... Familiarity with modern AI or machine learning systems is a plus, though not required. * Background ...

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All JobsBioinformatics Machine Learning JobsRemote Bioinformatics Machine Learning JobsRemote Bioinformatics Machine Learning Jobs in Washington

Remote Bioinformatics Machine Learning information

How do remote bioinformatics machine learning professionals typically collaborate with cross-functional teams?

Remote bioinformatics machine learning professionals often work closely with biologists, data scientists, and software engineers. Collaboration is typically facilitated through virtual meetings, shared code repositories, and project management tools. Regular communication is essential to align on data requirements, model development, and interpretation of results. While remote work offers flexibility, it requires strong organizational skills and proactive engagement to ensure seamless teamwork and project success.

What is a remote bioinformatics machine learning specialist?

A Remote Bioinformatics Machine Learning specialist is a professional who applies machine learning techniques to biological data, such as genomics or proteomics, while working from a remote location. They analyze complex biological datasets to uncover patterns, make predictions, and contribute to advancements in areas like drug discovery, disease research, and personalized medicine. These specialists typically have strong skills in programming, statistics, biology, and data analysis, and collaborate with researchers and healthcare professionals through digital communication tools.

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

To excel as a Remote Bioinformatics Machine Learning Specialist, a strong background in computational biology, statistics, and machine learning—often supported by an advanced degree in bioinformatics, computer science, or a related field—is essential. Proficiency with programming languages like Python or R, experience using machine learning frameworks (such as TensorFlow or scikit-learn), and familiarity with bioinformatics tools and databases are typically required. Excellent problem-solving, self-motivation, and clear communication skills help professionals collaborate effectively and independently in remote environments. These abilities are vital for developing accurate models, interpreting complex biological data, and contributing meaningful insights to scientific research.

What is the difference between Remote Bioinformatics Machine Learning vs Remote Computational Biologist?

AspectRemote Bioinformatics Machine LearningRemote Computational Biologist
Required CredentialsMaster's or PhD in Bioinformatics, Computer Science, or related fields; experience in machine learningMaster's or PhD in Biology, Bioinformatics, or related fields; strong computational skills
Work EnvironmentRemote, collaborative teams in biotech, pharma, or research institutionsRemote or on-site, working in research labs or academic settings
Industry UsageUsed in biotech, healthcare, and pharmaceutical industries for data analysis and model developmentCommon in academic research, biotech, and healthcare for biological data interpretation

Remote Bioinformatics Machine Learning focuses on developing algorithms and models to analyze biological data using machine learning techniques. In contrast, Remote Computational Biologist applies computational methods to biological research questions, often integrating diverse data types. Both roles require strong computational skills and often overlap, but the former emphasizes machine learning expertise, while the latter has a broader biological research scope.

What are the most commonly searched types of Bioinformatics Machine Learning jobs in Washington?

The most popular types of Bioinformatics Machine Learning jobs in Washington are:

What are popular job titles related to Remote Bioinformatics Machine Learning jobs in Washington?

For Remote Bioinformatics Machine Learning jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Remote Bioinformatics Machine Learning jobs in Washington look for?

The top searched job categories for Remote Bioinformatics Machine Learning jobs in Washington are:

What cities in Washington are hiring for Remote Bioinformatics Machine Learning jobs?

Cities in Washington with the most Remote Bioinformatics Machine Learning job openings:

Remote Bioinformatics Machine Learning jobs near you

Senior AI/ML Lead/ Developer (Remote)

Cognizant Technology Solutions

Washington, DC • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago

Cognizant rating

7.4

Company rating: 7.4 out of 10

Based on 85 frontline employees who took The Breakroom Quiz

52nd of 72 rated business consultants

Job description

Job Summary
We are seeking a highly skilled and innovative Senior AI/ML Engineer with strong expertise in Python, PySpark, Azure Machine Learning, Generative AI, and Full Stack Development to design and deliver advanced analytics and AI-driven solutions for global investment banking and brokerage operations.
The ideal candidate will combine deep technical expertise in machine learning, distributed computing, cloud-native AI platforms, and modern AI frameworks such as LangChain, LangGraph, RAG, Agentic AI Frameworks, FastAPI, and Azure OpenAI Service. This role requires close collaboration with business stakeholders to transform complex financial data into actionable insights that improve decision-making, reduce operational risk, and enhance operational efficiency.
Key Responsibilities
Machine Learning & Advanced Analytics
  • Design, develop, and deploy advanced machine learning models using Python and PySpark to analyze large-scale financial datasets and generate actionable business insights.
  • Build predictive, classification, clustering, anomaly detection, forecasting, and risk models supporting investment banking and brokerage functions.
  • Perform rigorous model validation, back-testing, and experimentation using historical and simulated market data.
  • Evaluate and implement appropriate statistical, machine learning, deep learning, and AI techniques based on business requirements and regulatory considerations.
  • Optimize model performance through feature engineering, hyperparameter tuning, algorithm enhancements, and distributed computing techniques.

Generative AI & Agentic Solutions
  • Design and implement enterprise-grade Generative AI solutions using Azure OpenAI Service.
  • Build and deploy Retrieval-Augmented Generation (RAG) applications leveraging vector databases and knowledge retrieval systems.
  • Develop intelligent agent-based systems using LangChain, LangGraph, and Agentic AI frameworks to automate business workflows and enhance decision support.
  • Apply Natural Language Processing (NLP), Large Language Models (LLMs), document intelligence, and conversational AI to streamline surveillance, reporting, compliance, and advisory functions.
  • Ensure safe, responsible, and governed adoption of Generative AI capabilities across the organization.

Python Full Stack Development
  • Design and develop scalable backend services and APIs using FastAPI.
  • Build microservices and AI application frameworks that integrate machine learning and GenAI capabilities into enterprise ecosystems.
  • Develop reusable and maintainable software components following modern software engineering best practices.
  • Implement API integrations, authentication mechanisms, monitoring, logging, and performance optimization strategies.

Data Engineering & MLOps
  • Design and implement scalable data pipelines and feature engineering workflows using Azure Machine Learning and cloud-native services.
  • Build reusable data products and machine learning components supporting multiple analytics and AI initiatives.
  • Partner with Data Engineering teams to operationalize machine learning models and AI applications.
  • Establish model monitoring, retraining strategies, experiment tracking, and lifecycle management processes.
  • Ensure solutions are secure, reliable, scalable, and production-ready.

Cloud & Azure AI Platform
  • Develop end-to-end ML and AI solutions using:
    • Azure Machine Learning
    • Azure OpenAI Service
    • Azure Data Lake
    • Azure Databricks
    • Azure Storage Services
    • Azure DevOps
  • Manage model deployment, monitoring, governance, and operationalization on Azure platforms.
  • Support enterprise-scale AI and analytics workloads while maintaining compliance and security standards.

Business Collaboration
  • Collaborate with product owners, business analysts, operations teams, and technology stakeholders to define high-value data science initiatives.
  • Translate complex investment banking and brokerage business challenges into measurable analytical solutions.
  • Present recommendations and analytical findings to both technical and non-technical audiences.
  • Drive adoption of AI and machine learning solutions through effective communication and stakeholder engagement.

Governance & Responsible AI
  • Promote responsible AI practices by evaluating model fairness, explainability, bias, security, and data quality.
  • Document assumptions, risks, methodologies, and limitations in a transparent and accessible manner.
  • Ensure adherence to regulatory requirements, model governance frameworks, and enterprise AI policies.

Leadership & Mentoring
  • Mentor junior data scientists, machine learning engineers, and developers.
  • Promote best practices in software development, experimentation, MLOps, AI engineering, and model governance.
  • Contribute to a culture of innovation, continuous learning, and technical excellence.

Required Qualifications
Technical Skills
  • 8+ years of experience in Data Science, Machine Learning, AI Engineering, or related fields.
  • Expert-level proficiency in Python and PySpark for large-scale data processing and model development.
  • Strong experience with:
    • FastAPI
    • REST APIs
    • Microservices Architecture
    • Object-Oriented Programming
    • Software Engineering Best Practices
  • Hands-on experience with:
    • LangChain
    • LangGraph
    • RAG Architectures
    • Agentic AI Frameworks
    • LLM Application Development
  • Strong expertise in:
    • Azure Machine Learning
    • Azure OpenAI Service
    • Azure Databricks
    • Azure Data Lake
    • MLOps and CI/CD Practices
  • Experience developing and deploying enterprise-grade AI/ML solutions in cloud environments.

Machine Learning & AI
  • Deep understanding of:
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
    • Ensemble Methods
    • NLP
    • Time-Series Forecasting
    • Anomaly Detection
    • Risk Modeling
  • Strong understanding of model evaluation, feature engineering, experimentation, validation, and explainability.

Domain Experience
  • Prior experience supporting:
    • Investment Banking
    • Capital Markets
    • Brokerage Operations
    • Trade Surveillance
    • Risk Management
    • Front Office or Middle Office Functions
  • Understanding of financial products, market data, and regulatory expectations is highly desirable.

Soft Skills
  • Excellent communication and stakeholder management skills.
  • Ability to explain complex technical topics to non-technical audiences.
  • Strong analytical and problem-solving capabilities.
  • Experience working effectively within distributed and hybrid teams.

Preferred Qualifications
  • Experience with vector databases such as Pinecone, Azure AI Search, Weaviate, or ChromaDB.
  • Knowledge of containerization technologies including Docker and Kubernetes.
  • Experience with CI/CD pipelines and DevOps practices.
  • Exposure to Responsible AI, Model Risk Management, and AI governance frameworks.
  • Azure certifications in AI, Data Science, or Machine Learning.

*Please note this role is not able to offer visa transfer or sponsorship now or in the future*
We're excited to meet people who share our mission and who can make an impact in a variety of ways. Don't hesitate to apply-even if you only meet the minimum requirements. Think about your transferable experiences and unique skills that make you stand out.
Salary and Other Compensation:
Applications will be accepted until Sept 07, 2026,
The annual salary for this position is between $ 90,000 - $ 150,000 depending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of Cognizant's applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
  • Medical/Dental/Vision/Life Insurance
  • Paid holidays plus Paid Time Off
  • 401(k) plan and contributions
  • Long-term/Short-term Disability
  • Paid Parental Leave
  • Employee Stock Purchase Plan

Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization's unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email [email protected] for roles based in the Americas or [email protected] for roles based in India.

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