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machine learning operations engineer

Hayden AI

<div>
 
 About Us
 
 
 
 At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.
 
 
 From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.
 
 
 
 Job Title:
 
 Machine Learning Operations Engineer
 
 
 
 Company:
 
 Hayden AI Technologies, Inc.
 
 
 
 Location:
 
 460 Bryant Street, Suite 100, San Francisco, CA 94107
 
 
 
 Position Duties
 
 
 
 <ul>
 <li>
 Optimize orchestration processes to ensure efficient deployment and management of AI models.
 </li>
 <li>
 Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance.
 </li>
 <li>
 Upgrade throughput to enhance scalability and responsiveness of AI systems.
 </li>
 <li>
 Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency.
 </li>
 <li>
 Ship new features and updates rapidly while maintaining high levels of quality and reliability.
 </li>
 <li>
 Deploy and monitor machine learning models produced by deep learning engineers.
 </li>
 <li>
 Design, deploy, and maintain performant and scalable processes for data acquisition and manipulation to enhance dataset accessibility.
 </li>
 <li>
 Participate actively in the team's software development process, including design reviews, code reviews, and brainstorming sessions.
 </li>
 <li>
 Maintain accurate and updated software development documentation.
 
 
 </li>
 </ul>
 
 Degree Requirements
 
 
 
 Bachelor of Science or Engineering degree or a foreign equivalent in Robotics, Machine Learning, Computer Science, Electrical Engineering, Electronics and Telecommunication Engineering, or a related field.
 
 
 
 Experience Requirement
 
 
 
 One (1) year of work experience in the job offered or as a Deep Learning Engineer, Perception Engineer II, MLOps, Research Engineer II, Machine Learning, Computer Vision Intern, or another related deep learning engineer role.
 
 
 
 Other Special Requirements
 
 
 
 <ul>
 <li>
 One (1) year of work experience with all of the following:
 </li>
 <ul>
 <li>
 Deploying real-world applied computer vision (including deep learning models) on edge devices
 </li>
 <li>
 Python programming and software design
 </li>
 <li>
 C++
 </li>
 <li>
 Software development tools and libraries including PyTorch, OpenCV, TensorFlow, MLflow
 </li>
 <li>
 Automated data annotation
 </li>
 <li>
 Distributed training in the cloud
 </li>
 <li>
 Deploying and managing GPU clusters for ML pipelines and workflows
 
 </li>
 </ul>
 </ul>
 
 Rate of Pay:
 
 $135,699.00 to $190,000.00 per year
 
 
 
 Location of Position and Interview:
 
 
 
 Hayden AI Technologies, Inc.
 
 
 460 Bryant Street, Suite 100
 
 
 San Francisco, CA 94107
 
 
 
 Applicants Should Submit Resumes To
 
 
 
 Janet Le-Mcintosh
 
 
 janet.le@hayden.ai
 
 
 Hayden AI Technologies, Inc.
 
 
 460 Bryant Street, Suite 100
 
 
 San Francisco, CA 94107
</div>

Machine Learning Operations Engineer

Aarki

<div>
 
 Who are we?
 
 
 
 Aarki is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.
 
 
 
 The role?
 
 
 
 We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.
 
 
 
 What will you do?
 
 
 
 <ul>
 <li>
 Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.
 </li>
 <li>
 Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
 </li>
 <li>
 Analyze the impact of integrating new data sources and features into our models.
 </li>
 <li>
 Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.
 </li>
 <li>
 Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.
 </li>
 <li>
 Document experiments, assumptions, and outcomes; maintain reproducibility
 
 
 </li>
 </ul>
 
 What are we looking for?
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Mathematics, Physics, Computer Science, or a related technical field.
 </li>
 <li>
 At least 2 years of professional experience in machine learning, statistical analysis, and data analysis.
 </li>
 <li>
 Experience with machine learning techniques such as regression, classification, and clustering.
 </li>
 <li>
 Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
 </li>
 <li>
 Strong grasp of probability, statistics, and data analysis principles.
 </li>
 <li>
 Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.
 
 
 </li>
 </ul>
 Nice-to-Have
 
 
 <ul>
 <li>
 Familiarity with system programming languages including C++ and Rust is a plus.
 </li>
 <li>
 Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
 </li>
 <li>
 Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.
 </li>
 </ul>
</div>

Machine Learning Engineer

Call For Referral

<div>
 
 
 Machine Learning Engineer
 
 
 
 
 Work Mode:
 
 
 Remote
 
 
 
 
 Compensation:
 
 
 $100&ndash;$120 per hour
 
 
 
 
 Work Type:
 
 
 Hourly, Independent
 
 
 
 
 Role Overview
 
 
 
 This role supports the evaluation of
 
 advanced machine learning systems
 
 in collaboration with
 
 leading AI research initiatives
 
 . It is intended for
 
 experienced machine learning engineers and researchers
 
 who can contribute to the development of
 
 high-quality evaluation suites
 
 that measure AI performance on
 
 real-world machine learning engineering tasks
 
 .
 
 
 The work focuses on translating
 
 practical ML research and engineering workflows
 
 into
 
 structured benchmarks
 
 for
 
 frontier models
 
 . This is a
 
 project-based, fully remote
 
 opportunity for professionals with
 
 strong hands-on ML research and engineering experience
 
 .
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 
 Design and develop
 
 detailed evaluation suites for machine learning engineering tasks
 </li>
 <li>
 
 Evaluate AI-generated solutions
 
 across model training, debugging, optimization, and experimentation workflows
 </li>
 <li>
 
 Analyze and document
 
 system behavior, performance characteristics, and limitations
 </li>
 <li>
 
 Contribute to the creation of benchmarks
 
 that reflect real-world ML engineering practices
 </li>
 <li>
 
 Maintain high standards
 
 of technical accuracy, clarity, and consistency in all deliverables
 </li>
 </ul>
 
 
 Qualifications
 
 
 <ul>
 <li>
 
 3+ years
 
 of experience in machine learning engineering or applied ML research
 </li>
 <li>
 
 Hands-on experience
 
 with model development, experimentation, and evaluation
 </li>
 <li>
 
 Background in ML research
 
 in an industry or academic setting (strongly preferred)
 </li>
 <li>
 
 Strong ability
 
 to reason about ML system design choices and technical trade-offs
 </li>
 <li>
 
 Excellent written communication skills
 
 and
 
 high attention to technical detail
 
 </li>
 </ul>
 
 
 Work &amp; Payment Details
 
 
 <ul>
 <li>
 You will operate as an
 
 independent professional
 
 </li>
 <li>
 This is a
 
 fully remote
 
 role with a
 
 flexible schedule
 
 </li>
 <li>
 Projects may be
 
 extended, shortened, or concluded
 
 based on needs and performance
 </li>
 <li>
 The work does
 
 not involve access to confidential or proprietary information
 
 from any employer, client, or institution
 </li>
 <li>
 
 Payments are made weekly
 
 via
 
 Stripe or Wise
 
 , based on services delivered
 </li>
 <li>
 Candidates requiring
 
 H1-B or STEM OPT
 
 sponsorship are
 
 not supported
 
 at this time
 </li>
 </ul>
 
 
 Application &amp; Onboarding Process
 
 
 <ul>
 <li>
 
 Upload your resume
 
 </li>
 <li>
 
 AI interview:
 
 A short
 
 15-minute
 
 conversational session to understand your background, experience, and interest in the role
 </li>
 <li>
 
 Follow-up communication
 
 within a few days with next steps and onboarding details
 </li>
 </ul>
 
 
</div>

Machine Learning Engineer (Remote)  $100 –$120/hr

Liftoff

<div><div>Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand.

Liftoff's solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence.</div>
About the Revenue Engine team

The Revenue Engine team works to understand the fundamental economics of the mobile ad tech marketplace, including the elasticity of demand and the effects of competition. The team of machine learning engineers, software engineers, and data analysts develops theories, validates those theories with experiments and analyses, and uses the learnings to build production systems that improve outcomes for Liftoff and its advertisers.

As a Machine Learning Engineer on the Revenue Engine team, you will:
<ul>
<li>Build statistical models and production systems to balance advertiser performance with business goals.</li>
<li>Tune optimization parameters, measure internal competition, and model dynamic environments.</li>
<li>Design and run experiments to validate theories underpinning the mobile ad tech economy.</li>
<li>Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.</li>
<li>Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).</li>
<li>Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.</li>
<li>Be part of an &quot;engineering excellence&quot; culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.</li>
</ul>
Requirements:
<ul>
<li>PhD in Computer Science, Machine Learning, Economics, or a related field.</li>
<li>Industry experience applying economics or machine learning to large scale problems.</li>
<li>Solid engineering and coding skills.</li>
<li>Excellent team communication and collaboration skills.</li>
<li>Experience with ad tech is a solid plus.</li>
</ul>
Location: 
This role is eligible for full-time remote work in one of our entities: CA, CO, ID, IL, FL, GA, MA, MI, MN, MO, NJ, NV, NY, OR, TX, UT, and WA.

We are a remote-first company with US hubs in Redwood City, Los Angeles, and New York City.

Travel Expectations: 
We offer several opportunities for in-person team gatherings, including but not limited to project meetings, regional meetups, and company-wide events. We expect our employees to attend these gatherings at least once per quarter. These gatherings provide essential opportunities for collaboration, communication, and team building.

#LI-EL1

#LI-REMOTE

We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process, we provide Covey with job requirements and candidate-submitted applications. We began using Covey Scout for Inbound on January 22, 2024.

Please see the independent bias audit report covering our use of Covey here.<div>
Liftoff offers a fast-paced, collaborative, and innovative work environment where employees are empowered to grow and make an impact. We're shaping the future of the mobile app ecosystem&mdash;join us and help accelerate what's next. 


Liftoff's compensation strategy includes competitive salaries, equity, and benefits designed to support employee well-being and performance. We benchmark compensation based on role, level, and location to ensure fairness and market alignment. Benefits may include medical coverage, wellness stipends, and additional perks based on your country of residence. 


Liftoff is an equal opportunity employer. We are committed to creating an inclusive environment for all employees and applicants regardless of race, ethnicity, national origin, age, marital status, disability, sexual orientation, gender identity, religion, veteran status, or any other characteristic protected by applicable law.

Agency and Third Party Recruiter Notice:

Liftoff does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or Recruiting Team. All candidates must be submitted via our Applicant Tracking System by approved Liftoff vendors who have been expressly requested to make a submission by our Recruiting Team for a specific job opening. No placement fees will be paid to any firm unless such a request has been made by the Liftoff Recruiting Team and such a candidate was submitted to the Liftoff Recruiting Team via our Applicant Tracking System.</div></div>

Uber

<div>About the Role
 
 
Earner Incentives is one of the fastest-growing products at Uber. As an Engineer on the Earner Incentives team you will work on the latest backend technologies developing highly scalable, Customer facing services and features, platforms and control panel tooling used by our operations on a daily basis.
 
 
Incentives Platform is used for generating efficient incentives for drivers, measuring progress of drivers towards achieving those incentives and then paying once incentive goals are achieved. It plays a critical role in ensuring adequate supply to meet the market demands across time and space. The combination of technical challenges and cross functional work will allow you to grow as a software engineer in any dimension.
 
 
Join us to work on some of the most exciting challenges on this journey.
 
 
What the Candidate Will Need / Bonus Points
- What the Candidate Will Do - 
 
 
<ul><li>As a backend engineer, you will architect, design and build software solutions to help with all aspects of capacity planning/management/engineering to scale Uber's infrastructure across a variety of sophisticated workflows and business processes.
</li><li>Design end-to-end features and systems to build high quality consumer-facing products.
</li><li>Write code, test, and maintain production services for high availability, reliability, and performance.
</li><li>Work with ML engineers and scientists to develop ML models to improve product performance.
</li><li>Work with Product Managers to understand product requirements and lead product rollout plans.
</li><li>Mentor junior team members.
</li><li>You will collaborate with other teammates to maintain the health of our systems and codebase, refactor and make improvements, and keep our systems robust and resilient.
</li></ul>- Basic Qualifications - 
 
 
<ul><li>2+ years experience working on the full software life cycle including gathering requirements, project planning, solution design, coding/implementation, testing, rollout/deployment and best practices as an individual contributor.
</li><li>Experience coding using general purpose programming language (eg. C/C++, Java, Python, Go, C#)
</li><li>Fast and passionate learner. Strong collaboration, documentation and communication skills.
</li></ul>- Preferred Qualifications - 
 
 
<ul><li>Experience building consumer-facing products.
</li><li>Experience designing and implementing large-scale service with excellent quality.
</li><li>Experience working with Machine Learning engineers and scientists on ML model productionization.
</li>Experience collaborating with other engineers and non-tech stakeholders, both to meet short-term goals and to create long-term partnerships. 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. For Seattle, WA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Software Engineer II - Earner Incentive

Arcade

<div>
 
 About Arcade
 
 
 
 Arcade is building the world&rsquo;s first AI physical product creation platform, where imagination becomes reality. Our platform lets anyone design, purchase, and sell custom, manufacturable products using natural language and generative AI. We believe everyone should have the power to create physical goods as easily as they post online, and we&rsquo;re building the infrastructure to make that real.
 
 
 We&rsquo;ve raised $42M from a world-class group of investors, including Reid Hoffman, Forerunner Ventures (Kirsten Green), Canaan Partners (Laura Chau), Adverb Ventures (April Underwood), Factorial Funds (Sol Bier), Offline Ventures (Brit Morin), Sound Ventures (Ashton Kutcher), Inspired Capital (Alexa von Tobel), and Torch Capital (Jonathan Keidan). Our angel investors include Elad Gil, Ev Williams, Marissa Mayer, Sara Beykpour, Kayvon Beykpour, Anna Veronika Dorogush, Eugenia Kuyda, David Luan, Sharon Zhou, Kelly Wearstler, Karlie Kloss, Colin Kaepernick, Christy Turlington Burns, and Jeff Wilke.
 
 
 Arcade is headquartered in San Francisco&rsquo;s Presidio and led by serial entrepreneur Mariam Naficy (Minted, Eve), and a founding team with deep experience in generative AI, design systems, and supply chain. We&rsquo;re pioneering a new category at the intersection of AI, personal expression, and on-demand manufacturing, and we&rsquo;re building fast.
 
 
 
 Overview Of Role
 
 
 
 We are seeking an Applied AI Engineer to join our growing team and help advance our generative AI capabilities. This role blends hands-on model development with integration of cutting-edge AI techniques into production systems. You&rsquo;ll contribute to research, model experimentation, and implementation working closely with cross-functional teams to bring AI-driven products to life.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Collaborate with engineering and product teams to train and fine-tune machine learning models, including diffusion models, LLMs, and other emerging generative architectures
 </li>
 <li>
 Develop tools and pipelines that support scalable, efficient, and production-ready AI systems
 </li>
 <li>
 Collect, clean, and analyze large-scale datasets to improve model performance and reliability
 </li>
 <li>
 Deploy and maintain AI models in production environments, ensuring scalability, reliability, and efficiency
 </li>
 <li>
 Design and deploy text-to-image models and LLM-based applications, including advanced prompt engineering, fine-tuning, and multi-component workflows
 </li>
 <li>
 Maintain and monitor deployed models in cloud-based production environments
 </li>
 <li>
 Stay current with developments in generative AI, contribute to the research environment, and apply new developments to enhance pipeline performance
 </li>
 <li>
 Communicate technical concepts clearly across technical and non-technical audiences
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Mathematics, AI/ML, or a related field
 </li>
 <li>
 Strong proficiency in Python and familiarity with Linux environments; hands-on experience with machine learning libraries like PyTorch, TensorFlow,, etc.
 </li>
 <li>
 Strong fundamentals in machine learning concepts, with a deep understanding of algorithms and their underlying mechanics ("under the hood")
 </li>
 <li>
 Familiarity with training, fine-tuning, and evaluating models, especially with large-scale datasets
 </li>
 <li>
 Familiarity with data preprocessing, cleaning, and feature engineering
 </li>
 <li>
 Awareness of recent developments in generative AI, diffusion models, and LLMs
 </li>
 <li>
 Familiarity with cloud platforms and deploying AI models in cloud-based environments
 </li>
 <li>
 Excellent problem-solving and analytical thinking skills with close attention to detail
 </li>
 <li>
 Excellent communication and collaboration skills in a fast-paced and collaborative environment.
 
 
 </li>
 </ul>
 
 Additional
 
 
 
 Competitive compensation
 
 
 Lunch provided daily
 
 
 Company events
 
 
 Arcade is an equal opportunity employer. We&rsquo;re committed to building a diverse, inclusive, and supportive team, and to creating a platform where anyone, anywhere, can make something meaningful.
 
 
</div>

Applied AI Engineer

<div>About the Role
 
 
Have you ever wondered why it's taking so long for an earner to be matched to your trip, why the ETA is so long, or how an Earner is picked from the many around you? If so, the Mobility Matching Science team is for you!
 
 
The Matching team at Uber builds the systems that determine the optimal way to fulfill trips on the Mobility platform. We work on the problems of determining which earners to send an offer to and when. The solutions we build are critical for maintaining reliability and ensuring the trust of riders and earners alike.
 
 
We are looking for experienced scientists who relish the opportunity to develop novel approaches and apply them at Uber's scale. They ideally have a good balance of causal inference, analysis, experimentation, and modeling knowledge, as well as, an ability to use these skills to identify business opportunities and deliver product recommendations.
 
 
What You'll Do
<ul><li>Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives
</li><li>Design and analyze experiments, communicating results that draw detailed and actionable conclusions
</li><li>Analyze and contribute to development of optimization algos and ML models for use in mobility matching
</li><li>Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product
</li></ul>Basic Qualifications
<ul><li>Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
</li><li>Minimum 2 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
</li><li>Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
</li><li>Proven experience in experimental design (e.g., A/B testing) and causal inference.
</li><li>Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
</li><li>Experience with exploratory data analysis, statistical analysis and testing, and model development.
</li></ul>Preferred Qualifications
<ul><li>Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
</li><li>Minimum 5 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
</li><li>Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
</li><li>Proven experience in experimental design (e.g., A/B testing) and causal inference.
</li><li>Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
</li>Experience with exploratory data analysis, statistical analysis and testing, and model development. 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Sr. Scientist - Mobility Matching

Explore 266+ Machine learning operations engineer in San Francisco this March. Find roles across top companies and apply now to boost your career.

266 machine learning operations engineer jobs In San Francisco

Machine Learning Operations Engineer

Machine Learning Engineer

Machine Learning Engineer (Remote) $100 –$120/hr

Machine Learning Engineer

Software Engineer II - Earner Incentive

Applied AI Engineer

Sr. Scientist - Mobility Matching