Remote AI Research Engineer Jobs
Remote AI Research Engineer jobs are in active demand at remote-first companies and large distributed teams, including employers like CVS Health, Alvarez & Marsal, and Netflix, from junior to senior. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 438+ Remote AI Research Engineer jobs
Title: AI Engineer
Duration: 6 Months
Location: Remote
Job Description:
Role Summary
We have a capable engineering team working on a complex legacy-to-Python migration and multiple customer-facing platforms. We don't need another developer — we need someone whose primary job is to make the entire team 3-5x faster by building AI-powered automation across every phase of the SDLC: requirements extraction, code generation, testing, defect analysis, and release. This person doesn't just use AI — they architect AI-driven engineering workflows that multiply the output of every developer on the team.
Required Skills (Must-Have):
- AI Agent Development & Orchestration: #1 differentiator. Proven experience building multi-step AI agent workflows for engineering tasks — chaining code analysis, generation, validation, feedback. GitHub Copilot, Claude, LangChain, custom agents, or equivalent.
- SDLC Automation & DevOps: Track record of automating significant portions of the delivery lifecycle — CI/CD, automated testing, code quality gates, release automation (GitHub Actions).
- Python (strong proficiency): Primary codebase is Python — data processing, JSON transformations, modular architecture. Must build tooling and review/generate code at scale.
- Automated Testing Frameworks: AI-augmented regression suites, diff/comparison tooling, scenario-based test generation, golden-file validation. pytest, CI integration.
- Advanced Prompt Engineering: Complex multi-step prompts for code generation, legacy code analysis, business rule extraction. Knows how to structure context, manage token limits, and validate AI outputs.
Requirements
- 7+ years of experience
Preferred Skills
- Legacy code analysis — Parsing unfamiliar proprietary languages to extract business logic via AI
- Java / Spring Boot — Secondary platforms are Java-based
- Elasticsearch, Azure / AKS / Cosmos DB — Platform technologies
- Healthcare data / MDM domain
Key Attributes
- AI-native thinker — Every manual process is an automation opportunity. AI is the primary development engine, not a side tool.
- Force multiplier — Measures success by how much faster they make the entire team, not by their own code output
- Builder of systems — Creates reusable tooling, agent templates, and playbooks the whole team adopts
- Pragmatic and fast — Ships working automation in days, not weeks. Iterates rapidly.
Environment
Python, Java/Spring Boot, Elasticsearch, Azure (AKS, Cosmos DB), GitHub, Jira, HPCC/ECL (legacy) | GitHub Copilot, Claude, SDD, AI-DLC | Agile, 2-week sprints
Title: AI Engineer
Duration: 6 Months
Location: Remote
Job Description:
Role Summary
We have a capable engineering team working on a complex legacy-to-Python migration and multiple customer-facing platforms. We don't need another developer — we need someone whose primary job is to make the entire team 3-5x faster by building AI-powered automation across every phase of the SDLC: requirements extraction, code generation, testing, defect analysis, and release. This person doesn't just use AI — they architect AI-driven engineering workflows that multiply the output of every developer on the team.
Required Skills (Must-Have):
- AI Agent Development & Orchestration: #1 differentiator. Proven experience building multi-step AI agent workflows for engineering tasks — chaining code analysis, generation, validation, feedback. GitHub Copilot, Claude, LangChain, custom agents, or equivalent.
- SDLC Automation & DevOps: Track record of automating significant portions of the delivery lifecycle — CI/CD, automated testing, code quality gates, release automation (GitHub Actions).
- Python (strong proficiency): Primary codebase is Python — data processing, JSON transformations, modular architecture. Must build tooling and review/generate code at scale.
- Automated Testing Frameworks: AI-augmented regression suites, diff/comparison tooling, scenario-based test generation, golden-file validation. pytest, CI integration.
- Advanced Prompt Engineering: Complex multi-step prompts for code generation, legacy code analysis, business rule extraction. Knows how to structure context, manage token limits, and validate AI outputs.
Requirements
- 7+ years of experience
Preferred Skills
- Legacy code analysis — Parsing unfamiliar proprietary languages to extract business logic via AI
- Java / Spring Boot — Secondary platforms are Java-based
- Elasticsearch, Azure / AKS / Cosmos DB — Platform technologies
- Healthcare data / MDM domain
Key Attributes
- AI-native thinker — Every manual process is an automation opportunity. AI is the primary development engine, not a side tool.
- Force multiplier — Measures success by how much faster they make the entire team, not by their own code output
- Builder of systems — Creates reusable tooling, agent templates, and playbooks the whole team adopts
- Pragmatic and fast — Ships working automation in days, not weeks. Iterates rapidly.
Environment
Python, Java/Spring Boot, Elasticsearch, Azure (AKS, Cosmos DB), GitHub, Jira, HPCC/ECL (legacy) | GitHub Copilot, Claude, SDD, AI-DLC | Agile, 2-week sprints
See All 438+ Remote AI Research Engineer Jobs
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Find JobsRemote AI Research Engineer Job Market
Who's Hiring
- CVS Health34
- Alvarez & Marsal26
- Netflix19
- Airbnb13
- Block9
Top Industries Hiring
- Technology & Software132
- Consulting & Professional Services59
- Healthcare & Medical Services54
- Investment & Asset Management29
- Accounting & Auditing27
What Employers Look For
The qualifications that appear most often in remote AI research engineer jobs.
- Master's or PhD in computer science, machine learning, or a related quantitative field
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience designing, training, and evaluating large-scale machine learning models
- Familiarity with distributed training systems and GPU cluster environments
- Strong mathematical foundation in linear algebra, probability, and optimization
- Demonstrated research output such as publications, preprints, or open-source contributions
Tips for Your Remote AI Research Engineer Job Search
Tailor your resume to model types
Hiring managers scan for the specific model architectures you've worked with, not just 'machine learning experience.' Name the frameworks, model families, and scale of training runs on your resume so reviewers can immediately match your background to their stack.
Build a public research artifact
A preprint, a well-documented GitHub repo, or a reproducible benchmark result carries more weight than coursework for AI research roles. Link to something you built or contributed to so hiring teams can evaluate your thinking before the first screen.
Apply early to roles that fit
Migrate Mate lists ai research engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter by research scope before applying
Some ai research engineer roles are fundamental research positions, others are applied or product-focused. Read job descriptions for phrases like 'publish at top venues' versus 'deploy to production' so you target openings that match the work you actually want to do.
Prepare a paper walkthrough for interviews
Most ai research engineer loops include a deep-dive on a paper you've authored or studied closely. Practice explaining the motivation, methodology, and limitations out loud, including what you'd do differently, because interviewers probe well beyond surface-level summaries.
Negotiate compute access alongside compensation
For research roles, GPU budget and cluster access directly affect what you can accomplish. Ask early in the offer stage about available compute resources, research cycles separate from product work, and publication policy so you understand the real conditions of the job.
Remote AI Research Engineer Jobs: Frequently Asked Questions
How do I get a remote AI research engineer job?
Target companies that already run distributed teams, since they hire remotely by default and know how to onboard someone they never meet in person. Remote AI research engineer employers screen hard for self-direction and clear written communication on top of the core skills, so show evidence you can own work without someone over your shoulder. Apply to the openings above that match your experience.
Which companies hire remote AI research engineers?
Employers currently hiring remote AI research engineers include CVS Health, Alvarez & Marsal, and Netflix, per current remote listings on Migrate Mate as of June 2026. Remote-first firms and large companies running distributed teams post the most remote AI research engineer roles.
Can you get a remote AI research engineer job with no experience?
Yes, but it is harder than an on-site role, because remote work expects you to operate independently from the start. Entry-level remote AI research engineer openings do exist, especially at remote-first companies, and a portfolio of real work helps more than a long resume. Applying broadly to the roles that fit improves your odds.
Do you need a degree for remote AI research engineer jobs?
Not always. Many employers hire remote AI research engineers on demonstrated skills and prior work rather than a specific degree, though some larger companies still prefer one. Showing relevant results matters more than a credential for most remote AI research engineer roles.
Which industries hire the most remote AI research engineers?
The sectors hiring the most remote AI research engineers are Technology & Software, Consulting & Professional Services, and Healthcare & Medical Services, based on current remote listings on Migrate Mate as of June 2026. These sectors run distributed teams and hire AI research engineers remotely most consistently.
See All 438+ Remote AI Research Engineer Jobs
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