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ai engineer – remote work Jobs in San Francisco

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ai research engineer, fair chemistry Jobs in San Francisco

ai research engineer, fair chemistry

research engineer

Omnifold

<div>Member of Technical Staff, ML Research
Omnifold trains custom AI models for each customer's supply chain - purpose-built systems that forecast demand, optimize decisions, and adapt continuously to a changing world. The research team is responsible for the core intelligence that makes this possible: developing new model architectures, curating proprietary data assets, and pushing the boundaries of what ML can do.
What makes this job interesting:<ul><li>
You will work on problems that frontier models can't solve. Supply chain dynamics require modeling physical systems and processes.</li><li>
You will own the full research cycle, from hypothesis to production model, with direct visibility into real-world impact.</li><li>
You will work at the intersection of statistical models, LLM reasoning capabilities, and proprietary data - a combination few research teams are building</li></ul>
What you'll own:<ul><li>
Designing and training models for forecasting and optimization across complex, multi-variable supply chain environments</li><li>
Building and curating proprietary data assets that carry signal about real-world physical and commercial systems</li><li>
Integrating LLM knowledge and reasoning capabilities into purpose-built models to maximize accuracy and adaptability</li><li>
Continuously improving model performance as market conditions shift (consumer sentiment, product launches, geopolitical changes, competitive dynamics)</li></ul>
What we're looking for:<ul><li>
Strong foundations in machine learning &mdash; experience developing and evaluating forecasting models, and LLM pipelines</li><li>
Deep understanding of time-series forecasting, optimization, or related domains</li><li>
Experience working with messy, heterogeneous real-world data</li><li>
PhD or equivalent research experience preferred, but exceptional engineers with relevant industry experience will be considered</li><li>
Comfort operating in a fast-moving, early-stage environment where research directly feeds production systems</li></ul>
Location: San Francisco (in-person, 5 days per week)
Omnifold&rsquo;s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.</div>

Research Engineer

Lightning AI

<div><div><h1 class="jobSectionHeader">Who We Are</h1>

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems&mdash;designed to take ideas from research to production with less friction.

Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.

We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.
<div>
<h1 class="jobSectionHeader">Our Values</h1>
</div>
<ul><li>Move Fast: We act with speed and precision, breaking down big challenges into achievable steps.
</li>
<li>Focus: We complete one goal at a time with care, collaborating as a team to deliver features with precision.
</li>
<li>Balance: Sustained performance comes from rest and recovery. We ensure a healthy work-life balance to keep you at your best.
</li>
<li>Craftsmanship: Innovation through excellence. Every detail matters, and we take pride in mastering our craft.
</li>
<li>Minimal: Simplicity drives our innovation. We eliminate complexity through discipline and focus on what truly matters.</li> 
</ul></div>
<h2 class="jobSectionHeader">What We're Looking For</h2>

We are seeking a highly skilled Lead Research Engineer to work on optimizing training and inference workloads on compute accelerators and clusters, through the Lightning Thunder compiler and the broader PyTorch Lightning ecosystem. This role sits at the intersection of deep learning research, compiler development, and large-scale system optimization. You'll be shaping technology that pushes the boundaries of model performance and efficiency, creating foundational software that will impact the entire machine learning ecosystem. 
 
You will be joining the Engineering Team and report to our Tech Lead. This is a hybrid role based in either our New York City or San Francisco office with in-office requirements of 2 days per week. The salary range for this role is $225,000-$275,000.
<h2 class="jobSectionHeader">
What you'll do</h2>
<ul><li>Develop performance-oriented model optimizations at multiple levels:</li>
<li><ul><li>Graph-level (e.g., operator fusion, kernel scheduling, memory planning)</li>
<li>Kernel-level (CUDA, Triton, custom operators for specialized hardware)</li>
<li>System-level (distributed training across GPUs/TPUs, inference serving at scale)</li>
</ul></li><li>Advance the Thunder compiler by building optimization passes, graph transformations, and integration hooks to accelerate training and inference workloads.</li>
<li>Work across the software stack to ensure optimizations are accessible to end users through clean APIs, automated tooling, and seamless integration with PyTorch Lightning. 
Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies.</li>
<li>Collaborate with hardware vendors and ecosystem partners to ensure Thunder runs efficiently across diverse backends (NVIDIA, AMD, TPU, specialized accelerators).</li>
<li>Contribute to open-source projects by developing new features, improving documentation, and supporting community adoption.</li>
<li>Engage with researchers and engineers in the community, providing guidance on performance tuning and advocating for Thunder as the go-to optimization layer in ML workflows.</li>
<li>Work cross-functionally with Lightning's product and engineering teams to ensure compiler and optimization improvements align with the broader product vision.</li>
</ul><h2 class="jobSectionHeader">What you'll need</h2>
<ul><li>Strong expertise with deep learning frameworks such as PyTorch</li>
<li>Hands-on experience with model optimization techniques, including graph-level optimizations, quantization, pruning, mixed precision, or memory-efficient training.</li>
<li>Knowledge of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling).</li>
<li>Familiarity with software engineering practices: designing APIs, building robust tooling, testing, CI/CD for performance-sensitive systems.</li>
<li>Excellent collaboration and communication skills, with the ability to partner across research, engineering, and external contributors.</li>
<li>Bachelor's degree in Computer Science, Engineering</li></ul> 

<h2 class="jobSectionHeader">
Nice to have</h2>
<ul><li>Experience with CUDA, Triton, or other GPU programming models for developing custom kernels.</li>
<li>Deep understanding of deep learning compiler internals (IR design, operator fusion, scheduling, optimization passes) or proven work in performance-critical software.</li>
<li>Proven track record contributing to open-source projects in ML, HPC, or compiler domains.</li>
<li>Advanced degree (Master's or PhD) in machine learning, compilers, or systems highly preferred.</li></ul><h2 class="jobSectionHeader"> 
</h2><div><h2 class="jobSectionHeader">
Benefits and Perks</h2>

We offer competitive base salaries and equity with a 25% one year cliff and monthly vesting thereafter. For our international employees, we work with our EOR to pay you in your local currency and provide equitable benefits across the globe.

In the US, we offer:
<ul>
<li>Medical, dental and vision</li>
<li>Life and AD&amp;D insurance</li>
<li>Flexible paid time off including winter closure</li>
<li>Paid family leave benefits</li>
<li>$500 monthly meal reimbursement, including groceries &amp; food delivery services</li>
<li>$500 one time home office stipend</li>
<li>$1,000 annual learning &amp; development stipend</li>
<li>100% Citibike membership (NYC only)</li>
<li>$45/month gym membership</li>
<li>Additional various medical and mental health services</li>
</ul>
At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.</div></div>

Lead Research Engineer

AI Research Engineer, FAIR Chemistry

OpenAI

About the Team
 
The Future of Computing Research team is an applied research team within the Consumer Devices group focused on developing new methods, models, and evaluation frameworks that support our vision for the future of computing. We work at the frontier of multimodal AI, helping turn emerging model capabilities into product experiences that are useful, delightful, and worthy of long-term trust.
 
Our work explores a new class of AI systems that can learn over time, adapt to individuals, and support people in the flow of daily life. This includes long-term memory, user modeling, and personalization systems that are aligned not just with immediate satisfaction, but with a person’s broader goals, values, and well-being.
 
We work closely across research, engineering, design, product, and safety to define what it means to build AI systems that know you over time, act at the right moment, and help in ways that are context-aware, respectful, and demonstrably beneficial.
 
About the Role
 
We are looking for a Research Engineer / Scientist to join the Future of Computing Research team to work on RLHF and post-training for personalized, multimodal AI systems.
 
This role will focus on building the learning and evaluation foundations that help models become more context-aware, adaptive, and useful over time. You will work on problems such as reward modeling, preference learning, long-horizon evaluation, and policy improvement for systems that must make high-quality behavioral decisions in realistic user settings. The work is deeply product-grounded: success is not just higher benchmark performance, but better model behavior in real-world use.
 
The ideal candidate is excited about pushing beyond one-turn assistant behavior toward systems that improve through feedback, learn from richer signals, and are trained against meaningful notions of user value. Internally, that maps closely to the need for careful reward design, feedback loops, and evaluation frameworks that test whether interventions are actually beneficial over longer horizons.
 
This role is based in San Francisco, CA. We use a hybrid work model of four days in the office per week and offer relocation assistance to new employees.
 
In this role, you will: 
Develop RLHF and post-training methods for multimodal models.
 
Build reward models and preference-learning pipelines for adaptive, personalized model behavior.
 
Design datasets, rubrics, and evaluation frameworks that capture user preferences, contextual appropriateness, and long-term value in realistic tasks.
 
Run experiments on policy improvement using explicit feedback, implicit signals, and model-based grading.
 
Work on long-horizon evaluation problems, where model quality depends not just on a single response but on whether behavior improves outcomes over time.
 
Collaborate closely with safety researchers to ensure that adaptation and personalization remain aligned, interpretable, and bounded by clear constraints.
 
Prototype and iterate quickly on training recipes, reward formulations, data pipelines, and evaluation suites for product-relevant behaviors.
 
Help define how OpenAI measures success for personalized AI systems including trust, appropriateness, and long-term user benefit.
 
You might thrive in this role if you: 
Have a strong background in machine learning research, with experience in RLHF, reward modeling, preference optimization, or post-training for large models.
 
Have worked on one or more of: reinforcement learning, ranking, recommender systems, personalization, memory, or human-in-the-loop evaluation.
 
Care about rigorous empirical work and know how to design clean experiments, reliable evals, and decision-useful metrics.
 
Are excited by the challenge of training models against nuanced behavioral objectives.
 
Have experience building datasets or eval pipelines grounded in human preferences, rubrics, or real-world product behavior.
 
Are comfortable working across the stack, from data generation and labeling strategy to training runs, reward functions, and analysis.
 
Are interested in multimodal AI and in how models can learn from richer interaction signals over time.
 
Want to work on product-shaping research with unusually high stakes for trust, alignment, and long-term user value.
 
Enjoy close collaboration with engineers, designers, and safety researchers to turn frontier research into real systems.
 
About OpenAI
 
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
 
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
 
For additional information, please see
 
OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement
 
.
 
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
 
To notify OpenAI that you believe this job posting is non-compliant, please submit a report through
 
this form
 
. No response will be provided to inquiries unrelated to job posting compliance.
 
We are committed to providing reasonable accommodations to applicants with disabilities.
 
OpenAI Global Applicant Privacy Policy
 
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
 
Compensation
 
$380K – $445K + Offers Equity

Research Engineer/Scientist - Human Alignment, Consumer Devices

Lila Sciences

<div>
 
 Your Impact at Lila
 
 
 
 We&rsquo;re seeking a Machine Learning Research Engineer for the Open-Endedness Team with expertise in large model training and optimizing novel algorithms for best results in distributed ML infrastructure. You&rsquo;ll design and maintain large-scale training systems, optimize performance for large models, and integrate cutting-edge techniques to improve efficiency and throughput.
 
 
 Open-Endedness is an emerging area of machine learning that aims to automate never-ending innovative processes of discovery and exploration. The Open-Endedness Team, led by Ken Stanley, investigates in particular how a continual chain of deep transformative creativity can be maintained that far exceeds the derivative creativity seen in current models. In effect, the systems developed on this team will go beyond simply solving problems posed by users, to conceiving the future unimagined directions of science itself.
 
 
 
 What You'll Be Building
 
 
 
 <ul>
 <li>
 Distributed training infrastructure for LLMs and multi-modal models.
 </li>
 <li>
 Performance optimizations for large-scale model training including training and optimization workflows (SFT, RL, long-context, etc.).
 </li>
 <li>
 Orchestrate frontier and open source LLMs along with complex compute-intensive tool use
 </li>
 <li>
 Scalable pipelines for data preprocessing and experiment orchestration, including tools for efficient data loading, pipeline parallelism, and optimizer tuning.
 </li>
 <li>
 System-level performance benchmarks and debugging utilities.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Need To Succeed
 
 
 
 <ul>
 <li>
 Proven experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray).
 </li>
 <li>
 Strong software engineering skills (Python, C++ kernel contributions are a plus).
 </li>
 <li>
 Understanding of large-scale model training techniques.
 </li>
 <li>
 Experience with cloud or HPC environments.
 
 
 </li>
 </ul>
 
 Bonus Points For
 
 
 
 <ul>
 <li>
 Prior work with scientific datasets or domain-specific modeling.
 </li>
 <li>
 Contributions to open-source ML frameworks.
 
 
 </li>
 </ul>
 
 About Lila
 
 
 
 Lila Sciences is the world&rsquo;s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai
 
 
 If this sounds like an environment you&rsquo;d love to work in, even if you only have some of the experience listed below, we encourage you to apply.
 
 
 
 We&rsquo;re All In
 
 
 
 Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
 
 
 Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
 
 
 
 A Note to Agencies
 
 
 
 
 Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science&rsquo;s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
 
 
 
 CompensationWe offer competitive compensation including bonus potential and generous early equity. The final offer will reflect your unique background, expertise, and impact.
 
 
 Expected Base Salary Range: $148,000 USD - $240,000 USD
</div>

Senior Machine Learning Research Engineer I/ II, Open-Endedness

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

<div>
 
 Why 11x Now
 
 
 
 At 11x, we're building autonomous digital workers that handle routine work end-to-end, freeing humans to focus on what they do best&mdash;creating, innovating, and building meaningful relationships. We're one of the world&rsquo;s fastest-growing AI companies. We've achieved a 20x increase in ARR and raised $75M+ from leading investors including a16z and Benchmark. Our team works in-person in our San Francisco office.
 
 
 
 About The Role
 
 
 
 We're looking for a fullstack engineer with a passion for applied research and cross-functional collaboration. In this role, you&rsquo;ll work closely with engineering leadership and our AI partners to prototype, build, and scale the next generation of autonomous digital workers.
 
 
 This is an immersive opportunity to deepen your expertise in large language model applications, system design, and human-AI interaction&mdash;while learning how fast-moving AI companies operate from the inside. You&rsquo;ll gain hands-on exposure to the latest model capabilities, contribute to internal experimentation, and help translate cutting-edge ideas into real-world tools.
 
 
 
 What You'll Do
 
 
 
 <ul>
 <li>
 Design, build, and iterate on fullstack systems that power AI agents from concept to deployment.
 </li>
 <li>
 Research and evaluate new approaches to agent orchestration, prompting strategies, and model alignment.
 </li>
 <li>
 Collaborate with product, design, and engineering teams to improve UX and usability for non-technical users.
 </li>
 <li>
 Share learnings, document findings, and contribute to the evolution of internal best practices.
 </li>
 <li>
 Participate in peer reviews, brainstorming sessions, and technical deep dives with our global team.
 
 
 </li>
 </ul>
 
 Ideal Candidates Have
 
 
 
 <ul>
 <li>
 3+ years of experience in a product-oriented software engineering role.
 </li>
 <li>
 A strong foundation in Typescript, React, NextJS, and NodeJS.
 </li>
 <li>
 Experience working in fast-paced environments with a track record of rapid learning and iteration.
 </li>
 <li>
 Interest in research, technical writing, or contributing to knowledge-sharing within a team.
 </li>
 <li>
 Bonus: experience with large language models, prompt engineering, or AI infrastructure.
 
 
 </li>
 </ul>
 Base salary range: $150,000 &ndash; $250,000
 
 
 <ul>
 <li>
 Final offer will be based on a variety of factors, including role level, relevant experience, skills, and job-related expertise.
 
 
 </li>
 </ul>
 
 11x is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.
 
</div>

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