Remote Senior ML Engineer Jobs
Remote Senior ML Engineer jobs are in active demand across the U.S., with remote-first firms and distributed engineering teams leading hiring in tech, fintech, and healthcare AI. Employers posting remote senior ml engineer openings right now include Atlas Technologies, Atlassian, and Lockheed Martin. Find a role that fits below and apply directly.
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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.
See All 222+ Remote Senior ML Engineer Jobs
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Find JobsRemote Senior ML Engineer Job Market
Who's Hiring
- Atlas Technologies22
- Atlassian15
- Lockheed Martin12
- Whatnot9
- Indeed6
Top Industries Hiring
- Technology & Software22
- Hospitality & Tourism5
- Retail3
- Biotechnology & Pharmaceuticals3
- Consulting & Professional Services3
What Employers Look For
The qualifications that appear most often in remote senior ML engineer jobs.
- 5 or more years of hands-on machine learning engineering experience in production environments
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX
- Experience designing and deploying end-to-end ML pipelines at scale
- Familiarity with MLOps tooling including experiment tracking, model registries, and CI/CD for models
- Strong understanding of distributed computing and cloud platforms such as AWS, GCP, or Azure
- Graduate degree in computer science, statistics, or a related quantitative field preferred
Tips for Your Remote Senior ML Engineer Job Search
Signal async fluency in your application materials
Remote hiring teams want proof you can collaborate without real-time back-and-forth. Show written design docs, RFC-style project write-ups, or documented experiment results in your portfolio so interviewers can evaluate your thinking before the first call.
Apply early to remote roles that fit
Migrate Mate lists remote senior ml engineer openings from across the U.S. in one place, so you can find roles that match your stack and seniority level and apply directly without sorting through on-site listings mixed in.
Highlight production ML ownership in your resume
Remote teams hire senior ml engineers to own model lifecycles independently. Call out specific systems you took from prototype to production, the MLOps tooling you used, and how you monitored and maintained models post-deployment, not just experiment accuracy.
Prepare for asynchronous technical screenings
Many remote-first companies send take-home ML case studies or ask you to review a model architecture and respond in writing. Practice explaining your reasoning in clear, structured prose, since written communication carries more weight in remote interviews than in on-site loops.
Remote Senior ML Engineer Jobs: Frequently Asked Questions
How do I get a remote senior ml engineer job?
Remote senior ml engineer roles go to candidates who can demonstrate self-direction, clear async written communication, and ownership of end-to-end ML systems without daily oversight. Remote-first companies and distributed teams screen heavily for experience shipping production models, familiarity with tools like MLflow, Weights and Biases, and cloud ML platforms, and the ability to unblock themselves. A strong GitHub portfolio with documented projects and evidence of cross-functional async collaboration gives you a clear edge.
Which companies hire remote senior ml engineers?
Remote senior ml engineer roles are posted by Atlas Technologies, Atlassian, and Lockheed Martin and others right now, based on current remote listings on Migrate Mate as of November 2026. These include remote-first tech companies, distributed fintech and healthtech teams, and AI-native startups that hire senior ml engineers entirely outside a central office.
Can you get a remote senior ml engineer job with no experience?
Yes, but remote entry into senior ml engineer roles is harder because remote teams expect independent execution from day one. Remote-first startups and AI product companies are the most open to candidates without formal experience if you can show production-quality personal or open-source ML projects, documented async workflows, and measurable results. A public portfolio with reproducible experiments and clear written documentation does more work than a degree or a prior job title.
Do you need a degree for remote senior ml engineer jobs?
Not always. Remote employers hiring senior ml engineers weigh demonstrated ability to build and deploy models over formal credentials, particularly at remote-first companies where output is the only visible signal. What opens doors is a strong portfolio of shipped ML work, fluency with current frameworks and MLOps tooling, and evidence that you can communicate technical decisions clearly in writing without a manager in the next seat.
Which industries hire the most remote senior ml engineers?
The sectors hiring the most remote senior ml engineers are Technology & Software, Hospitality & Tourism, and Retail, based on current remote listings on Migrate Mate as of November 2026. These industries rely on distributed engineering teams where senior ml engineers contribute across time zones without needing to be co-located with data infrastructure or product teams.
See All 222+ Remote Senior ML Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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