Remote AI ML Platform Jobs
Remote AI ML Platform jobs are open across the U.S. in sectors like cloud infrastructure, enterprise software, and data-intensive industries, at companies ranging from remote-first AI startups to large distributed engineering teams. Employers hiring remotely right now include Affirm, reddit, and Stack AV. See the openings below and apply to the ones that match your experience.
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About Stack:
Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands.
If you are an Internal Candidate, please apply via our Internal Job Board.
About the Role:
In the ML Data Understanding team, our mission is to provide trusted and useful data to efficiently power all of Stack's ML applications end-to-end from mining to training to safety evaluation. We work hand-in-hand with AV autonomy teams to provide cutting-edge solutions to all their data needs, working across data engineering, mining, modeling and infrastructure. In particular, we provide services to find (data mining), curate (datasets), annotate (data labeling), search and serve (high throughput data access) data for all ML needs.
- Data Mining: We are building a framework and infrastructure to find interesting events quickly and flexibly. As part of this mission, you would be setting the direction for and helping us build an inference service using LLMs, open-world models and vector databases.
- Semantic Search for Data Mining: We are building the infrastructure of a highly scalable semantic search service for multimodal data to find interesting events quickly and flexibly. As part of this mission, you would be setting the direction for and helping us build an inference service using the latest AI models & approaches.
- Dataset management for training: We are building state of the art infrastructure to support machine learning training and inference workloads using OSS components such as Ray, Spark, Lance and Iceberg.
Responsibilities:
- Leverage AI-assisted software engineering to architect next-generation multimodal semantic search and data discovery platforms for autonomous driving development.
- Investigate and apply frontier large language models, visual language models, and state-of-the-art Retrieval Augmented Generation (RAG) techniques.
- Build out agentic data mining, semantic search, and data curation workflows as part of a recursive self-improvement machine learning loop.
- Develop data understanding platform infrastructure for real-time querying/vector databases and batch/stream processing using technologies like Ray, Spark, LanceDB, or similar.
- Deliver end-to-end data mining solutions that span onboard (C++) and offboard (ML & Data Infra) infrastructure to accelerate AV product development.
- Develop e2e solution for real-time semantic search services (text/images/videos) and vector DBs.
- Discover and identify key issues in existing ML infra and proactively improve system performance.
- Build low latency/high throughput batch or stream processing pipelines.
- Drive technical discussions across multiple orgs and deliver solutions on a timely basis.
- Write readable and high-performance Python/C++ code.
Qualifications:
- Experience with both ML platforms and building ML-based applications.
- 5+ years of relevant domain experience.
- Proven track record of building scalable, reliable infrastructure in a fast-paced environment.
- Deep understanding of design trade-offs with the ability to articulate those trade-offs and achieve alignment with others.
- Hands-on background in developing semantic search infrastructure, generating multimodal (video/image/audio) embeddings, and managing vector databases.
- Experience with multi-modal data indexing, model training infrastructure, model optimization, or large data processing pipelines.
- Experience in building ML models or infrastructure in autonomous vehicles, perception, and decision-making domains (desirable but not required).
#LI-AW1
We are proud to be an equal opportunity workplace. We believe that diverse teams produce the best ideas and outcomes. We are committed to building a culture of inclusion, entrepreneurship, and innovation across gender, race, age, sexual orientation, religion, disability, and identity.
Check out our Privacy Policy.
Please Note: Pursuant to its business activities and use of technology, Stack AV complies with all applicable U.S. national security laws, regulations, and administrative requirements, which can restrict Stack AV's ability to employ certain persons in certain positions pursuant to a range of national security-related requirements. As such, this position may be contingent upon Stack AV verifying a candidate's residence, U.S. person status, and/or citizenship status. This position may also involve working with software and technologies subject to U.S. export control regulations. Under these regulations, it may be necessary for Stack AV to obtain a U.S. government export license prior to releasing its technologies to certain persons. If Stack AV determines that a candidate's residence, U.S. person status, and/or citizenship status will require a license, prohibit the candidate from working in this position, or otherwise be subject to national security-related restrictions, Stack AV expressly reserves the right to either consider the candidate for a different position that is not subject to such restrictions, on whatever terms and conditions Stack AV shall establish in its sole discretion, or, in the alternative, decline to move forward with the candidate's application.
About Stack:
Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands.
If you are an Internal Candidate, please apply via our Internal Job Board.
About the Role:
In the ML Data Understanding team, our mission is to provide trusted and useful data to efficiently power all of Stack's ML applications end-to-end from mining to training to safety evaluation. We work hand-in-hand with AV autonomy teams to provide cutting-edge solutions to all their data needs, working across data engineering, mining, modeling and infrastructure. In particular, we provide services to find (data mining), curate (datasets), annotate (data labeling), search and serve (high throughput data access) data for all ML needs.
- Data Mining: We are building a framework and infrastructure to find interesting events quickly and flexibly. As part of this mission, you would be setting the direction for and helping us build an inference service using LLMs, open-world models and vector databases.
- Semantic Search for Data Mining: We are building the infrastructure of a highly scalable semantic search service for multimodal data to find interesting events quickly and flexibly. As part of this mission, you would be setting the direction for and helping us build an inference service using the latest AI models & approaches.
- Dataset management for training: We are building state of the art infrastructure to support machine learning training and inference workloads using OSS components such as Ray, Spark, Lance and Iceberg.
Responsibilities:
- Leverage AI-assisted software engineering to architect next-generation multimodal semantic search and data discovery platforms for autonomous driving development.
- Investigate and apply frontier large language models, visual language models, and state-of-the-art Retrieval Augmented Generation (RAG) techniques.
- Build out agentic data mining, semantic search, and data curation workflows as part of a recursive self-improvement machine learning loop.
- Develop data understanding platform infrastructure for real-time querying/vector databases and batch/stream processing using technologies like Ray, Spark, LanceDB, or similar.
- Deliver end-to-end data mining solutions that span onboard (C++) and offboard (ML & Data Infra) infrastructure to accelerate AV product development.
- Develop e2e solution for real-time semantic search services (text/images/videos) and vector DBs.
- Discover and identify key issues in existing ML infra and proactively improve system performance.
- Build low latency/high throughput batch or stream processing pipelines.
- Drive technical discussions across multiple orgs and deliver solutions on a timely basis.
- Write readable and high-performance Python/C++ code.
Qualifications:
- Experience with both ML platforms and building ML-based applications.
- 5+ years of relevant domain experience.
- Proven track record of building scalable, reliable infrastructure in a fast-paced environment.
- Deep understanding of design trade-offs with the ability to articulate those trade-offs and achieve alignment with others.
- Hands-on background in developing semantic search infrastructure, generating multimodal (video/image/audio) embeddings, and managing vector databases.
- Experience with multi-modal data indexing, model training infrastructure, model optimization, or large data processing pipelines.
- Experience in building ML models or infrastructure in autonomous vehicles, perception, and decision-making domains (desirable but not required).
#LI-AW1
We are proud to be an equal opportunity workplace. We believe that diverse teams produce the best ideas and outcomes. We are committed to building a culture of inclusion, entrepreneurship, and innovation across gender, race, age, sexual orientation, religion, disability, and identity.
Check out our Privacy Policy.
Please Note: Pursuant to its business activities and use of technology, Stack AV complies with all applicable U.S. national security laws, regulations, and administrative requirements, which can restrict Stack AV's ability to employ certain persons in certain positions pursuant to a range of national security-related requirements. As such, this position may be contingent upon Stack AV verifying a candidate's residence, U.S. person status, and/or citizenship status. This position may also involve working with software and technologies subject to U.S. export control regulations. Under these regulations, it may be necessary for Stack AV to obtain a U.S. government export license prior to releasing its technologies to certain persons. If Stack AV determines that a candidate's residence, U.S. person status, and/or citizenship status will require a license, prohibit the candidate from working in this position, or otherwise be subject to national security-related restrictions, Stack AV expressly reserves the right to either consider the candidate for a different position that is not subject to such restrictions, on whatever terms and conditions Stack AV shall establish in its sole discretion, or, in the alternative, decline to move forward with the candidate's application.
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Find Remote AI ML Platform JobsRemote AI ML Platform Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Media & Entertainment
What Employers Look For
The qualifications that appear most often in remote AI ML platform jobs.
- Proficiency in Python and experience building or maintaining ML pipelines at scale
- Hands-on experience with containerization and orchestration tools such as Docker and Kubernetes
- Familiarity with at least one managed ML platform such as SageMaker, Vertex AI, or Azure ML
- Experience designing or operating distributed training and model serving infrastructure
- Understanding of CI/CD principles applied to model training, evaluation, and deployment workflows
- Bachelor's or master's degree in computer science, engineering, or a closely related quantitative field
Tips for Your Remote AI ML Platform Job Search
Show your async collaboration skills upfront
Remote ai ml platform teams run on written communication. Highlight experience with async-first tools like Confluence, Notion, or Slack documentation in your materials. Employers screening for remote readiness look for candidates who can unblock themselves and communicate decisions clearly in writing.
Build a public portfolio of platform work
Remote hiring managers can't watch you work, so a public GitHub with real MLOps pipelines, feature stores, or model serving infrastructure does the work for you. Documented projects that show you've built and maintained shared ML platform components are far more persuasive than a job title alone.
Apply early to remote roles that fit
Migrate Mate lists remote ai ml platform openings from across the U.S. in one place. Search the current openings, find roles that match your stack and seniority, and apply directly. Remote roles often close faster than on-site ones because the candidate pool is national.
Prepare for a remote-native technical interview
Remote ai ml platform interviews typically include live system design sessions over video with shared diagramming tools like Miro or Excalidraw. Practice talking through distributed training infrastructure, model serving architectures, and pipeline observability out loud, since remote interviewers are assessing how clearly you communicate technical decisions.
Remote AI ML Platform Jobs: Frequently Asked Questions
How do I get a remote ai ml platform job?
Remote ai ml platform roles go to candidates who can demonstrate self-directed execution and strong async communication alongside deep technical skills. Remote-first software companies and distributed AI teams are the most consistent hirers. Showing proficiency in MLOps tooling, model deployment pipelines, and infrastructure-as-code gives you a concrete edge, and a portfolio of shipped platform work is far more persuasive than credentials alone when you're competing for a fully remote seat.
Which companies hire remote ai ml platforms?
Companies hiring remote ai ml platforms right now include Affirm, reddit, and Stack AV, based on current remote listings on Migrate Mate as of November 2026. Remote-first technology firms and distributed engineering teams across cloud services, enterprise AI, and data infrastructure are the most consistent sources of these openings.
Can you get a remote ai ml platform job with no experience?
Yes, but remote entry-level ai ml platform roles are harder to land because employers expect you to work independently from day one with minimal hand-holding. Your best path is contributing to open-source MLOps or platform projects, building a public portfolio of pipeline or infrastructure work, and targeting smaller remote-first AI companies that are more willing to invest in developing junior platform engineers.
Do you need a degree for remote ai ml platform jobs?
Not always. Remote employers hiring ai ml platform engineers weigh demonstrated skills, shipped platform projects, and familiarity with tools like Kubernetes, Kubeflow, Ray, or cloud ML services more heavily than a diploma. A strong GitHub history, contributions to ML infrastructure projects, or certifications in cloud and MLOps can substitute effectively, especially at remote-first companies that evaluate candidates primarily on technical output.
Which industries hire the most remote ai ml platforms?
Most remote ai ml platform openings sit in Technology & Software and Media & Entertainment, per current remote listings on Migrate Mate as of November 2026. These sectors rely on distributed engineering teams that build and maintain shared ML infrastructure across multiple product lines and geographies, making remote platform roles structurally natural.
See All 12 Remote AI ML Platform Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote AI ML Platform Jobs