Remote Associate Machine Learning Engineer Jobs
Remote associate machine learning engineer jobs are open across the U.S. at remote-first firms and distributed engineering teams building production ML systems in software, finance, healthcare, and beyond. Employers hiring remotely right now include Atlas Technologies, Atlassian, and Lockheed Martin. See the openings below and apply to the ones that match your experience.
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Showing 5 of 251+ Remote Associate Machine Learning Engineer jobs
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.
See All 251+ Remote Associate Machine Learning Engineer Jobs
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Find JobsRemote Associate Machine Learning Engineer Job Market
Who's Hiring
- Atlas Technologies22
- Atlassian15
- Lockheed Martin12
- Whatnot10
- Indeed6
Top Industries Hiring
- Technology & Software23
- Hospitality & Tourism5
- Retail3
- Automotive3
- Electronics & Hardware3
What Employers Look For
The qualifications that appear most often in remote associate machine learning engineer jobs.
- Bachelor's or master's degree in computer science, statistics, or a related quantitative field
- Proficiency in Python and at least one ML framework such as PyTorch or TensorFlow
- Experience building, training, and evaluating machine learning models on real datasets
- Familiarity with SQL and data preprocessing tools for cleaning and feature engineering
- Understanding of core ML concepts including supervised learning, model evaluation, and regularization
- Exposure to version control systems and collaborative development workflows such as Git
Tips for Your Remote Associate Machine Learning Engineer Job Search
Build a portfolio built for async review
Remote hiring managers evaluate your work independently, so every project needs a clear README, documented methodology, and reproducible results. Prioritize end-to-end notebooks and ML pipelines on GitHub that a reviewer can run and understand without asking you a single question.
Demonstrate written communication skills upfront
Remote associate machine learning engineer roles rely on async tools like Slack, Confluence, and pull request reviews instead of in-person discussion. Referencing these tools in your resume and cover letter signals you can operate effectively inside a distributed team from your first week.
Apply early to remote roles that fit
Migrate Mate lists remote associate machine learning engineer openings from across the U.S. in one place, so you can find roles that match your skills and apply directly without sorting through roles that aren't remote or aren't at the right level.
Prepare for remote-first technical interviews
Many distributed teams conduct live coding sessions in shared environments like CoderPad or Jupyter notebooks over video call. Practice explaining your reasoning out loud while coding, since remote interviewers judge both your technical approach and your ability to collaborate through a screen.
Remote Associate Machine Learning Engineer Jobs: Frequently Asked Questions
How do I get a remote associate machine learning engineer job?
Target companies with established distributed engineering teams, since they already have the infrastructure and culture to onboard remote ML engineers successfully. Remote employers screen heavily for self-direction, clear async written communication, and hands-on skills like Python, PyTorch or TensorFlow, and ML pipeline tooling. A portfolio of reproducible projects on GitHub that shows end-to-end model work gives you a concrete edge over candidates with similar resumes.
Which companies hire remote associate machine learning engineers?
Employers currently hiring remote associate machine learning engineers include Atlas Technologies, Atlassian, and Lockheed Martin, per current remote listings on Migrate Mate as of November 2026. Remote-first software companies, fintech firms, and distributed AI-focused teams across healthcare and enterprise SaaS consistently post associate-level ML roles that are fully remote.
Can you get a remote associate machine learning engineer job with no experience?
Yes, but remote entry-level ML roles are harder to land because employers expect you to work independently without close in-person oversight from day one. Your strongest substitute for experience is a public portfolio showing real ML work, such as end-to-end projects with documented experiments, clean code, and clear write-ups. Remote-first startups and product teams building new ML capabilities are more open to junior remote hires than large enterprises with structured in-office rotations.
Do you need a degree for remote associate machine learning engineer jobs?
Not always. Many remote employers weigh demonstrated skills, a strong GitHub portfolio, and practical experience with ML frameworks alongside or instead of a formal degree. That said, roles at larger technology and finance companies often list a bachelor's in computer science, mathematics, or a related field as a baseline. If you don't have a degree, completing recognized ML courses and contributing to open-source projects can substitute effectively for many remote-first teams.
Which industries hire the most remote associate machine learning engineers?
Most remote associate machine learning engineer openings sit in Technology & Software, Hospitality & Tourism, and Retail, per current remote listings on Migrate Mate as of November 2026. These sectors invest heavily in distributed engineering teams because their ML workloads, model training pipelines, and data infrastructure can run entirely in the cloud with no need for on-site presence.
See All 251+ Remote Associate Machine Learning Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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