Sr Staff Machine Learning Engineer Jobs
Sr Staff Machine Learning Engineer jobs are open across technology, financial services, healthcare, and autonomous systems, from senior to principal and distinguished levels, with specializations in large-scale model training, MLOps infrastructure, and applied NLP or computer vision. Find a role that fits from the openings below and apply directly.
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Team Description:
The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation.
Role Description:
We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers.
Responsibilities:
- Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders.
- Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit's advertising surfaces.
- Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value.
- Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth.
- Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs.
- Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes.
- Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration.
- Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel.
- Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership.
- Mentor ML engineers and help grow the team's expertise in retrieval, recommendation, and representation learning.
Required Qualifications:
- 7+ years of industry experience, including substantial experience building and shipping applied ML products.
- Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems.
- Strong understanding of retrieval modeling concepts, including DNN, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval.
- Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks.
- Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration.
- Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics.
- Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines.
- Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code.
- Technical leadership experience: setting direction, leading complex projects, influencing partner teams, and mentoring other engineers.
- Excellent written and verbal communication, with the ability to explain complex modeling choices to technical and non-technical audiences.
Preferred Qualifications:
- Experience with ads retrieval, ad serving, recommendation, search relevance, or marketplace optimization
- Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals
- Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experience outcomes
- Experience in ads marketplaces at peer companies
- Publications, patents, or industry contributions in applied ML or ranking systems
- Experience with sequential modeling (e.g., RNNs, Transformers)
Benefits:
- 100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)
- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
#LI-AJ1
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.
Team Description:
The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation.
Role Description:
We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers.
Responsibilities:
- Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders.
- Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit's advertising surfaces.
- Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value.
- Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth.
- Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs.
- Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes.
- Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration.
- Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel.
- Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership.
- Mentor ML engineers and help grow the team's expertise in retrieval, recommendation, and representation learning.
Required Qualifications:
- 7+ years of industry experience, including substantial experience building and shipping applied ML products.
- Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems.
- Strong understanding of retrieval modeling concepts, including DNN, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval.
- Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks.
- Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration.
- Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics.
- Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines.
- Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code.
- Technical leadership experience: setting direction, leading complex projects, influencing partner teams, and mentoring other engineers.
- Excellent written and verbal communication, with the ability to explain complex modeling choices to technical and non-technical audiences.
Preferred Qualifications:
- Experience with ads retrieval, ad serving, recommendation, search relevance, or marketplace optimization
- Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals
- Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experience outcomes
- Experience in ads marketplaces at peer companies
- Publications, patents, or industry contributions in applied ML or ranking systems
- Experience with sequential modeling (e.g., RNNs, Transformers)
Benefits:
- 100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)
- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
#LI-AJ1
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.
Sr Staff Machine Learning Engineer Jobs by Experience Level
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Find JobsSr Staff Machine Learning Engineer Job Market
Who's Hiring
- Apple20
- General Motors12
- GEICO11
- ServiceNow11
- Google10
Top Industries Hiring
- Technology & Software68
- Electronics & Hardware27
- Automotive17
- Banking & Financial Services14
- Insurance10
What Employers Look For
The qualifications that appear most often in sr staff machine learning engineer jobs.
- PhD or Master's degree in computer science, statistics, or a closely related field
- 8 or more years of industry experience building and shipping production machine learning systems
- Deep expertise in distributed training frameworks such as PyTorch, TensorFlow, or JAX
- Experience designing and operating large-scale ML infrastructure including feature stores and model serving
- Demonstrated technical leadership across cross-functional teams including research, engineering, and product
- Proficiency in MLOps tooling including experiment tracking, model registries, and CI/CD pipelines for ML
Tips for Your Sr Staff Machine Learning Engineer Job Search
Quantify model impact on your resume
Hiring committees at this level want to see the business outcome, not just the architecture. Replace generic bullets with metrics tied to latency reduction, revenue lift, or compute cost savings your models delivered at scale.
Showcase cross-functional technical leadership
Sr staff roles require you to drive decisions across research, product, and platform teams. Your resume and cover letter should name specific initiatives where you set the technical direction and influenced headcount or roadmap prioritization.
Filter openings by stack before applying
Job descriptions at this level vary widely between PyTorch-heavy research shops and TensorFlow-plus-Vertex production environments. Match your depth in distributed training frameworks, feature stores, and serving infrastructure to what each listing explicitly calls out.
Apply early to roles that fit
Migrate Mate lists sr staff machine learning engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a system design narrative for interviews
Expect a multi-hour design loop where you architect an end-to-end ML system under real constraints. Practice explaining your tradeoff reasoning aloud, especially around data pipelines, model versioning, and online versus offline inference decisions.
Negotiate scope and resources alongside compensation
At the sr staff level, your offer negotiation should include compute budget, team size, and reporting structure. Clarify whether the role owns a platform team or embeds in product, since that distinction shapes your long-term influence and career trajectory.
Sr Staff Machine Learning Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most sr staff machine learning engineers?
The companies hiring the most sr staff machine learning engineers right now include Apple, General Motors, and GEICO, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of September 2026. Demand is concentrated in companies scaling foundation models, autonomous systems, and enterprise AI platforms.
How many sr staff machine learning engineer jobs are remote?
About 63% of sr staff machine learning engineer openings are fully remote or hybrid as of September 2026, reflecting strong employer flexibility at this seniority level. Roles focused on applied NLP, recommendation systems, and MLOps infrastructure tend to offer the highest share of remote arrangements, while positions tied to robotics or on-device inference more often require on-site presence.
How do you become a sr staff machine learning engineer?
Reaching the sr staff level typically requires building a record of end-to-end ML system ownership, moving from implementing models to designing the infrastructure and processes that support them at scale. You need demonstrated influence beyond your immediate team, such as setting technical standards, mentoring staff engineers, or driving org-wide architectural decisions. A graduate degree accelerates the path but sustained production impact matters more.
Can you get hired as a sr staff machine learning engineer with little experience?
Direct placement into a sr staff role without substantial experience is uncommon, but candidates from adjacent paths can be competitive. A strong open-source contribution record on widely used ML frameworks, published research with measurable adoption, or leadership of a high-visibility ML platform at a smaller company can substitute for years of traditional industry tenure when the technical depth is evident.
What does the sr staff machine learning engineer interview process look like?
The process typically includes a recruiter screen, a technical phone interview covering ML fundamentals and past system design decisions, and a multi-day on-site or virtual loop. The loop usually combines a large-scale ML system design session, a coding round focused on data manipulation and model evaluation, a research or paper discussion, and a leadership interview assessing cross-functional influence and how you handle technical disagreements with senior stakeholders.
Where can I find and apply to sr staff machine learning engineer jobs?
You can find and apply to sr staff machine learning engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your background and apply directly to each one that fits.
See All 278+ Sr Staff Machine Learning Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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