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ai application engineer

ai researcher

Stealth AI Startup

<div>
 
 About the Role
 
 
 We are seeking an exceptional Lead Researcher to lead and scale a world-class AI research organization focused on advancing the state of the art in machine learning, large language models, and applied AI systems. This role is both
 
 strategic and hands-on
 
 , responsible for defining long-term research vision while actively contributing to breakthrough innovations that directly impact product and business outcomes.
 
 
 As Head of Research, you will build, mentor, and manage a high-caliber team of researchers and research engineers, partner closely with executive leadership, and translate cutting-edge research into scalable, real-world systems.
 
 
 Key Responsibilities
 
 <ul>
 <li>
 Define and own the
 
 research vision and roadmap
 
 , aligned with company strategy and long-term goals
 </li>
 <li>
 Lead and grow a team of
 
 AI Researchers, Applied Scientists, and Research Engineers
 
 </li>
 <li>
 Drive innovation across areas such as:
 </li>
 <li>
 Large Language Models (LLMs)
 </li>
 <li>
 Reasoning and planning systems
 </li>
 <li>
 Representation learning
 </li>
 <li>
 Robustness, safety, and evaluation
 </li>
 <li>
 Multimodal and agent-based systems
 </li>
 <li>
 Balance
 
 long-horizon exploratory research
 
 with
 
 near-term applied research
 
 that powers products
 </li>
 <li>
 Ensure research excellence through strong scientific rigor, reproducibility, and evaluation standards
 </li>
 <li>
 Collaborate with Product, Engineering, and Leadership to transition research into production
 </li>
 <li>
 Represent the company externally through publications, conferences, and industry engagement
 </li>
 <li>
 Establish best practices for research processes, tooling, and infrastructure
 </li>
 <li>
 Mentor senior researchers and develop future research leaders
 </li>
 </ul>
 
 Required Qualifications
 
 <ul>
 <li>
 PhD in Computer Science, Machine Learning, AI, or a closely related field (or equivalent research experience)
 </li>
 <li>
 0-7 years of experience in AI/ML research, with at least several years in a
 
 research role
 
 </li>
 <li>
 Proven track record of
 
 high-impact research contributions
 
 (top-tier conferences, journals, or widely adopted systems)
 </li>
 <li>
 Deep expertise in modern ML techniques, especially
 
 LLMs, deep learning, and foundation models
 
 </li>
 <li>
 Experience leading and scaling research teams in industry or advanced research labs
 </li>
 <li>
 Strong understanding of the tradeoffs between research innovation and production constraints
 </li>
 <li>
 Ability to communicate complex technical ideas clearly to both technical and non-technical stakeholders
 </li>
 </ul>
 
 Preferred Qualifications
 
 <ul>
 <li>
 Experience leading research at a top AI lab, FAANG company, or well-known AI startup
 </li>
 <li>
 Publications at leading venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)
 </li>
 <li>
 Experience with model training at scale, including distributed systems and ML infrastructure
 </li>
 <li>
 Background in deploying research models into real-world products
 </li>
 <li>
 Prior experience influencing company-level technical strategy
 </li>
 </ul>
 
 What We Offer
 
 <ul>
 <li>
 
 Base salary up to $500,000
 
 , plus competitive equity
 </li>
 <li>
 Opportunity to shape the future direction of cutting-edge AI research
 </li>
 <li>
 Access to significant compute and research resources
 </li>
 <li>
 Collaboration with elite engineers, researchers, and product leaders
 </li>
 <li>
 A culture that values deep thinking, experimentation, and long-term impact
 </li>
 </ul>
 
 
</div>

AI Scientist - LLMs [32814]

Handshake

<div>
 
 Overview
 
 
 
 Handshake is recruiting exceptional graduates with a master&rsquo;s in Pharmacy (MPharm, PharmD, or equivalent) to join our AI research community. This program brings subject-matter experts together to enhance the capabilities of Large Language Models (LLMs) within specialized domains and subdomains. The Handshake AI Fellowship program runs year-round, though project availability will vary by domain.
 
 
 
 About Handshake AI
 
 
 
 About Handshake AI
 
 
 Details
 
 
 <ul>
 <li>
 The position is remote and asynchronous - work independently from wherever you are.
 </li>
 <li>
 Flexible hours and the ability to work remotely, with no minimum commitment to hours.
 </li>
 <li>
 Project work includes and is not limited to developing domain-specific prompts and evaluating LLM responses.
 </li>
 <li>
 Dedicate time researching topics that are interesting to you, with the assistance of AI.
 </li>
 <li>
 Learn new skills while contributing to the changing world of AI across various disciplines.
 </li>
 <li>
 Placement into a project will be dependent on project availability.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Must have a master's from a US institution
 </li>
 <li>
 Able to participate in primarily asynchronous work in partnership with leading AI labs.
 </li>
 <li>
 Believe your expertise can outmatch current AI systems in understanding and explaining key concepts in your field.
 
 
 </li>
 </ul>
 Selection for each project is limited to a small cohort chosen for their disciplinary and research expertise. We are seeking experts in new research areas to test the limits of the world&rsquo;s top LLMs&rsquo; scientific and language understanding by completing tasks that impact the next frontier AI model.
 
 
 Work authorization information
 
 
 F-1 students who are eligible for CPT or OPT may be eligible for projects on Handshake AI. Work with your Designated School Official to determine your eligibility. If your school requires a CPT course, Handshake AI may not meet your school&rsquo;s requirements. STEM OPT is not supported. for more information on what types of work authorizations are supported on Handshake AI.
</div>

Pharmacy Expert

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

Sutter Health

We are so glad you are interested in joining Sutter Health!
 
Organization: 
SHSO-Sutter Health System Office-Bay
 
Position Overview: 
****Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.****
 
Responsible for leading the design, build, and maintain Sutter Health’s artificial intelligence (AI) and machine learning (ML) infrastructure, including end to end pipelines for ML and large language models (LLM) that support analytics, data science, and enterprise AI use cases. This includes how AI models and associated data are ingested, processed, trained, deployed, monitored, governed, and secured across cloud and on premises environments. Ensures that AI systems meet high standards of performance, reliability, and compliance, enabling the organization to safely and effectively integrate AI capabilities into clinical, operational, and strategic workflows.
 
Utilizing modern artificial intelligence operations (AIOps) and machine learning operations (MLOps) practices, leads the operationalization of models, maintenance of scalable AI services, monitoring of system behavior, and automation of deployment workflows. Works with structured and unstructured data, clinical data models, healthcare data standards, and modern cloud platforms. Develops and validates ML models, integrates researcher built or vendor provided algorithms, and contributes to AI platform architecture and tooling. Sets standards for high quality, secure, and well governed AI systems that support advanced analytics, automation, and intelligent applications.
 
Job Description: 
****Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.****
 
EDUCATION: 
<ul>
<li>Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field; or equivalent combination of education and experience.</li>
</ul>
TYPICAL EXPERIENCE: 
<ul>
<li>5 years recent relevant experience</li>
</ul>
PREFERRED EXPERIENCE: 
<ul>
<li>Advanced experience with Azure cloud services, including containerized model hosting, Azure ML, secure environment management, and cloud‑native deployment patterns.</li>
<li>Strong proficiency in MLOps / LLMOps practices, including CI/CD pipelines, automated testing, observability, model versioning, canary/AB rollouts, and drift monitoring.</li>
<li>Hands‑on expertise in building and operating AI/ML/LLM pipelines (ingestion → preprocessing → training → inference → monitoring), using Python, modern container frameworks, and orchestration systems.</li>
<li>Experience integrating researcher‑built or vendor‑provided ML/LLM models into production workflows, including API‑based embedding, performance tuning, and secure data handling.</li>
<li>Familiarity with healthcare data standards and environments, such as clinical data models, unstructured clinical text, and privacy/security expectations in regulated domains.</li>
</ul>
SKILLS AND KNOWLEDGE: 
<ul>
<li>Relevant experience in AI/ML Engineering and LLM/MLOps</li>
<li>Experience building end to end AI/ML pipelines, including training, deployment, monitoring, and retraining loops.</li>
<li>Strong programming skills in Python and familiarity with modern ML/LLM frameworks and libraries.</li>
<li>Hands on experience with Azure, including services such as Fabric, Foundry, Container Apps, AKS, and Application Insights.</li>
<li>Experience implementing AIOps / MLOps / LLM Ops practices, including CI/CD pipelines, automated testing, observability, and versioned deployments.</li>
<li>Experience with GitOps principles using GitHub Actions or similar tools.</li>
<li>Understanding of healthcare data models, including Epic Clarity, FHIR, HL7, and Caboodle.</li>
<li>Ability to evaluate and integrate machine learning models into production systems and support model lifecycle management.</li>
<li>Strong debugging skills across distributed systems, containerized environments, and cloud platforms.</li>
<li>Ability to work in cross functional environments with clinicians, data scientists, architects, and engineering teams.</li>
<li>Ability to produce high quality documentation and communicate complex technical concepts to diverse stakeholders.</li>
<li>Detail oriented, organized, and effective at prioritizing multiple concurrent initiatives.</li>
<li>Familiarity with HIPAA, PHI/PII security requirements, and regulatory considerations for healthcare AI.</li>
</ul>
These Principal Accountabilities, Requirements and Qualifications are not exhaustive but are merely the most descriptive of the current job. Management reserves the right to revise the job description or require that other tasks be performed when the circumstances of the job change (for example, emergencies, staff changes, workload, or technical development).
 
Job Shift: 
Days
 
Schedule: 
Full Time
 
Days of the Week: 
Monday - Friday
 
Weekend Requirements: 
As Needed
 
Benefits: 
Yes
 
Unions: 
No
 
Position Status: 
Exempt
 
Weekly Hours: 
40
 
Employee Status: 
Regular
 Sutter Health is an equal opportunity employerEOE/M/F/Disability/Veterans.
 
Pay Range is $168,958.40 to $270,337.60 / annual salary
 The compensation range may vary based on the geographic location where the position is filled. Total compensation considers multiple factors, including, but not limited to a candidate’s experience, education, skills, licensure, certifications, departmental equity, training, and organizational needs. Base pay is only one component of Sutter Health’s comprehensive total rewards program. Eligible positions also include a comprehensive benefits package.

AI 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

AI Research Engineer, FAIR Chemistry

About the Team: 
Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity.
 
Codex Security is OpenAI’s first security agent, built to scan source code repositories, validate real vulnerabilities, and integrate with Codex to help generate fixes.
 
About the Role: 
We’re looking for a Technical Lead Manager (TLM) to lead the Codex Security team. In this role, you will own the technical direction of the product, lead a team of engineers and researchers building agentic security systems, and work closely with customers adopting Codex security.
 
This role blends technical leadership, product thinking, and security expertise. You’ll guide the development of AI-driven security research capabilities while ensuring the system delivers real value to organizations securing large-scale codebases.
 
What you’ll do: 
Lead and grow a team building Codex Security, OpenAI’s agentic security researcher.
 
Set the technical direction for systems that analyze large codebases, surfacing higher-confidence findings and automatically generate fixes.
 
Design architectures for agent-based security workflows combining LLM reasoning, source code analysis, and developer tooling.
 
Work closely with product, research, and GTM teams to shape Codex Security’s roadmap and enterprise adoption.
 
Partner with enterprise customers to understand real-world security challenges and ensure Codex Security solves them effectively.
 
Drive high engineering standards across reliability, security, and performance.
 
Translate advances in AI agents into practical tools for developers and security teams.
 
You may thrive if you: 
Have led engineering teams building complex technical systems, ideally in security, developer tooling, or AI.
 
Have strong technical depth in software engineering, distributed systems, or application security.
 
Are comfortable working across product, research, and customer-facing workstreams.
 
Enjoy translating ambiguous problems into concrete technical systems.
 
Are excited about applying AI agents to real-world security problems.
 
Can balance long-term technical vision with rapid iteration on a product used by customers.
 
Communicate clearly with both engineers and enterprise stakeholders.
 
Goals & impact
 
Development of high-confidence vulnerability discovery and automated patch generation system across millions of repos, significantly reducing false positives and enabling engineering teams to ship secure code faster while minimizing triage overhead
 
Outcomes include: more resilient AI architectures, reduced exploit windows, and better-targeted security R&D investments across enterprises and consumers
 
Key technical challenges
 
High-precision security detection at scale: Designing pipelines that scan millions of commits while maintaining a high signal-to-noise ratio—minimizing false positives and over-reported severity while still catching rare but critical vulnerabilities.
 
Context-aware threat modeling: Automatically constructing and evolving project-specific threat models that capture system trust boundaries, assets, and attack surfaces, and using them to guide vulnerability discovery and prioritization.
 
Automated vulnerability validation: Building sandboxed environments and validation workflows that can reproduce and pressure-test potential vulnerabilities to distinguish real exploits from speculative findings.
 
Automated patch generation with minimal regressions: Generating secure, context-aware code fixes that align with system intent and surrounding logic, ensuring patches improve security without breaking functionality.
 
Operating large-scale security agents: Running agentic security workflows across millions of commits and repositories while maintaining reliability, performance, and cost efficiency.
 
Human–AI security collaboration: Designing feedback loops where developer input (e.g., severity adjustments and triage decisions) continuously improves the system’s detection accuracy and prioritization.
 
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
 
$490K – $515K

TLM, Codex Security

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