Remote - Ontario, Canada
Open 6 months, first seen by us 207 days agoListed location: Remote - Ontario, CanadaRead the full posting
Design, build, and deploy production-grade ML models and systems at scale
Own the full ML lifecycle from feature engineering to training and monitoring
Build scalable data and model pipelines with automated retraining
Improve system performance across latency, throughput, and model quality
Partner with Product, Data Science, Infrastructure, and Engineering teams
Design, build, and deploy production-grade machine learning models and systems at scale
Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
Build scalable data and model pipelines with strong reliability, observability, and automated retraining
Improve system performance across latency, throughput, and model quality metrics
Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions
3-5+ years of experience building, deploying, and operating machine learning systems in production
Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
Experience improving measurable metrics through applied machine learning
Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems
Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
Experience working with real-time systems and low-latency production environments
Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
From the employer’s posting.
Open 6 monthsWe first saw this posting 207 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 207
Up longer than most. Similar jobs usually come down between day 7 and day 71.
Well past Reddit’s usual: its jobs come down by about day 48.
From 4,682 similar postings that came down, and 33 of Reddit’s. Coming down is not always a hire.
43 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 3-5+ years of experience building, deploying, and operating machine learning systems in production - Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals - ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
- Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems - Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.) - Experience working with real-time systems and low-latency production environments
- ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs) - Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow) - Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
- Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals - ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs) - Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
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Already applied? Track this application
Yes. We’ve followed one other Senior Machine Learning Engineer job there since April 2026. It is still up.
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning.
We hire Machine Learning Engineers across both our Consumer and Ads organizations, giving you the opportunity to work on a wide range of high-impact problems across the Reddit ecosystem.
We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, — and who want to help shape the future of discovery, relevance, and monetization at Reddit.
If you love working on complex, real-world ML problems at massive scale, this role is for you.
What You’ll Work On
As a Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including:
You’ll work on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes.
What You’ll Do
Basic Qualifications
Preferred Qualifications
Potential Teams
Benefits
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.
During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.
Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.
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Remote - Ontario, Canada
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