AI Research Scientist Remote Jobs in Arizona (US)

View details: Senior AI Researcher

Role Description We're hiring our first dedicated AI Researcher to advance the core models powering Ares. You'll work alongside our VP of AI Engineering and a small AI engineering team, with direct collaboration with our CEO — a researcher and practitioner with 26 years of offensive security experience, contributions to the OWASP API Security Top 10, and a permanent exhibit at The Mob Museum. This is a research role, not an applied ML role. You'll own original research on offensive security agents — how they reason, plan, use tools, and operate autonomously over long horizons. You'll design experiments end-to-end, build the evaluation infrastructure the field doesn't yet have, and translate research wins into capability that ships. The feedback loop is fast and adversarial. Research that proves out goes into production. Research that doesn't gets killed quickly so the next bet can start. What You'll Do - Drive original research on offensive security agents — reasoning, planning, tool use, and autonomous long-horizon operation - Advance Dagger's post-training pipeline: supervised fine-tuning, RL from verifier signals, LoRA adaptation, and evaluation against adversarial benchmarks - Extend Javelin's co-evolutionary self-training architecture: curriculum design, self-play dynamics, and reward modeling for security-specific outcomes - Design and execute experiments end-to-end, from hypothesis through writeup - Build internal evaluation harnesses that measure capability rigorously, where no public benchmark exists - Translate research into production handoffs to AI Engineering — model cards, deployment notes, and known failure modes - Contribute to Assail's external research voice through papers, talks, responsible disclosures, and technical writing - Collaborate with engineering teammates on research methodology and experimental design Qualifications - Original ML research output — published papers, widely cited preprints, significant open-source releases, or shipped research that materially advanced a production system - Hands-on post-training experience with language models at the 7B+ parameter scale, end-to-end ownership of a pipeline including data, training, and evaluation - Direct work with at least one of: RL from verifier or reward signals, preference optimization (DPO/IPO/KTO), or supervised fine-tuning with synthetic data pipelines - Experience with agentic LLM systems — tool use, multi-step reasoning, planning, or long-horizon execution - Ability to design evaluation that measures real capability and avoids contamination or specification gaming - Strong Python and PyTorch, with experience in distributed training at multi-GPU scale - Clear technical writing — research memos, experiment writeups, papers, or equivalent Requirements - Working knowledge of offensive security fundamentals (we'll teach you the rest if you bring strong ML depth) - Prior work on code-generating or code-reasoning models - Experience with sparse, delayed, or expensive reward signals in RL - Research on robustness, adversarial ML, or red-teaming of language models - Familiarity with long-horizon agent benchmarks (SWE-bench, Cybench, WebArena, or similar) What This Role Will Teach You - How to train and post-train capable models in a narrow, high-stakes domain - How to design evaluation that holds up to scrutiny when no benchmark exists yet - How agentic systems behave under adversarial conditions — including failure modes that don't appear in benign settings - The full offensive security stack — API, web, and mobile — at a depth most ML researchers never reach - How to make publication and disclosure decisions for dual-use research - How research moves from hypothesis to production in a small team where the handoff is measured in days Benefits - Competitive base salary and meaningful early-stage equity - Comprehensive health and dental coverage - Unlimited paid time off, including parental leave - Conference, publication, and continued learning budget — we want you engaged with the research community - The chance to work on a problem that matters, with people who care about doing it well

AI AI/ML Dagger LLM Python PyTorch

United States

Staff AI Researcher

Aledade

Self-described as "a new company with an old-fashioned goal," Aledade aims to put healthcare control back into the hands of doctors. Headquartered in Bethesda, Maryland, the compan

AI Research Scientist3 days ago

AI AI/ML Python Distributed Systems Apache Spark

Location: Staff AI Researcher Workplace: remote Category: Engineering Job Description: Job Duties/Description: The Staff AI Researcher is responsible for developing advanced artificial intelligence solutions that improve health outcomes for millions of patients by empowering primary care physicians with technology that keeps patients healthy and prevents unnecessary hospitalizations. The Staff AI Researcher collaborates with engineering and analytics teams to bring AI technologies into existing products and workflows. Additionally, the role involves training, fine-tuning, and using AI models harnessing knowledge from extensive data sets of medical records, diagnoses, claims, and prescriptions collected from millions of patients across the country. Primary duties include building working prototypes using off-the-shelf and novel AI techniques to deliver higher levels of optimization for the company; working with large, complex data sets and solving difficult, non-routine analytical problems to harvest data; redesigning existing pipelines and systems to meet growing data and query needs; implementing techniques for fine-tuning and adapting pre-trained generative models to specific healthcare domains or tasks; developing evaluation metrics and benchmarks to assess the quality and performance of AI/ML models; designing and implementing feature engineering pipelines, including data processing, feature extraction, and transformation to optimize model performance; setting and upholding standards for engineering processes, including style and code checking, test harnesses, and release packaging; and delivering working proof-of-concept solutions that balance speed, scalability, and time-to-market considerations. This is a remote work position. Multiple positions are available. Minimum Requirements Must have a Master’s degree or foreign degree equivalent in Computer Science or a related quantitative field and six (6) years of machine learning and statistical analysis experience. The position also requires demonstrated knowledge and experience with the following: three (3) years of deep learning and large language model experience; three (3) years of Python experience; three (3) years of proficiency in selecting the right tools given a data optimization problem; addressing challenges from incomplete, unrepresentative, and mislabeled data; and large-scale distributed systems at scale and statistical software (e.g., Spark). This is a remote work position. Who We Are: Aledade, a public benefit corporation, exists to empower the most transformational part of our health care landscape - independent primary care. We were founded in 2014, and since then, we've become the largest network of independent primary care in the country - helping practices, health centers and clinics deliver better care to their patients and thrive in value-based care. Additionally, by creating value-based contracts across a wide variety of health plans, we aim to flip the script on the traditional fee-for-service model. Our work strengthens continuity of care, aligns incentives and ensures primary care physicians are paid for what they do best - keeping patients healthy. If you want to help create a health care system that is good for patients, good for practices and good for society - and if you're eager to join a collaborative, inclusive and remote-first culture - you've come to the right place. What Does This Mean for You? At Aledade, you will be part of a creative culture that is driven by a passion for tackling complex issues with respect, open-mindedness and a desire to learn. You will collaborate with team members who bring a wide range of experiences, interests, backgrounds, beliefs and achievements to their work - and who are all united by a shared passion for public health and a commitment to the Aledade mission. In addition to time off to support work-life balance and enjoyment, we offer the following comprehensive benefits package designed for the overall well-being of our team members: - Flexible work schedules and the ability to work remotely are available for many roles - Health, dental and vision insurance paid up to 80% for employees, dependents and domestic partners - Robust time-off plan (21 days of PTO in your first year) - Two paid volunteer days and 11 paid holidays - 12 weeks paid parental leave for all new parents - Six weeks paid sabbatical after six years of service - Educational Assistant Program and Clinical Employee Reimbursement Program - 401(k) with up to 4% match - Stock options - And much more! At Aledade, we don’t just accept differences, we celebrate them! We strive to attract, develop and retain highly qualified individuals representing the diverse communities where we live and work. Aledade is committed to creating a diverse environment and is proud to be an equal opportunity employer. Employment policies and decisions at Aledade are based on merit, qualifications, performance and business needs. All qualified candidates will receive consideration for employment without regard to age, race, color, national origin, gender (including pregnancy, childbirth or medical conditions related to pregnancy or childbirth), gender identity or expression, religion, physical or mental disability, medical condition, legally protected genetic information, marital status, veteran status, or sexual orientation. Privacy Policy: By applying for this job, you agree to Aledade's Applicant Privacy Policy available at https://www.aledade.com/privacy-policy-applicants

View details: Staff AI Researcher

Worldwide

Principal AI/ML Researcher – Reasoning, Planning, and Decision-making Systems

Airbnb

Airbnb is a community based on connection and belonging.

AI Research Scientist5 days ago

Full Time Remote LeadTeam 5,001-10,000Since 2007H1B Sponsor

View details: Principal AI/ML Researcher – Reasoning, Planning, and Decision-making Systems

• Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks at scale. • Advance techniques in LLM/LRM post-training, reinforcement learning–based decisioning, and knowledge-integrated agents. • Design methods for plan induction, value estimation, and contingency modeling within intelligent agents. • Explore and validate protocols for distributed reasoning and joint planning among cooperative agents in multi-agent systems. • Architect RPD systems that integrate post-trained LLMs/LRMs, graph-structured memory (e.g., KGs), and RL-driven controllers. • Design recursive task planners, search-based or policy-based reasoners, and belief-state trackers that can interoperate with large model substrates. • Build and evolve stateful, dynamic models that combine supervised learning with online/offline reinforcement, simulation-based rollouts, and symbol grounding. • Set direction for planning/reasoning infrastructure within the AI/ML platform strategy.

Java Python PyTorch Ray

United States

$296K - $370K / year

Bright Vision Technologies

AI Research Engineer (Applied AI)

AI Research Scientist7 days ago

Full Time Remote Mid Level

Role Description We are seeking an AI Research Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems. The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines. The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement. Key Responsibilities - Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. - Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies. - Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. - Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. - Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools. - Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. - Optimize models for accuracy, latency, throughput, and cost based on production requirements. - Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality. - Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements. - Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions. - Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences. - Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. - Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. - Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities. Qualifications - Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience. - Six or more years of combined research and applied ML engineering experience. - Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX. - Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale. - Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML. - Experience taking ML models from research prototype to production with appropriate observability and safeguards. - Familiarity with distributed training, mixed-precision training, and accelerator hardware. - Strong written and verbal communication skills, including ability to explain complex methods clearly. - Demonstrated ability to read, evaluate, and adapt techniques from current research literature. - Track record of shipping impactful applied AI projects. Preferred Qualifications - Published research at top-tier AI/ML venues. - Experience with large language model training, fine-tuning, or evaluation. - Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures. - Exposure to responsible AI, model evaluation, and alignment practices. - Experience contributing to open-source ML projects. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 650-6699. Learn more about Bright Vision Technologies at www.bvteck.com .

AI AI/ML Observability/Monitoring Python PyTorch JAX

View details: AI Research Engineer (Applied AI)

United States

$100K - $150K / year

Bright Vision Technologies

AI Research Engineer, Applied AI

AI Research Scientist7 days ago

AI AI/ML Observability/Monitoring Python PyTorch JAX

Title: AI Research Engineer (Applied AI) Location: Remote US Remote Full Time Experienced Job Description: Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled AI Research Engineer (Applied AI) to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. AI Research Engineer (Applied AI) Job Title: AI Research Engineer (Applied AI) Location: 100% Remote (Continental United States) Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor) Experience: 6+ years Salary: 100K – 150K Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party) Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap Compensation: Competitive base salary commensurate with experience, plus benefits. Employment Terms & Visa Policy This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role. BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience. Job Summary We are seeking an AI Research Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems. The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines. The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement. Key Responsibilities - Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. - Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies. - Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. - Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. - Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools. - Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. - Optimize models for accuracy, latency, throughput, and cost based on production requirements. - Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality. - Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements. - Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions. - Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences. - Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. - Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. - Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities. Required Qualifications - Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience. - Six or more years of combined research and applied ML engineering experience. - Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX. - Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale. - Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML. - Experience taking ML models from research prototype to production with appropriate observability and safeguards. - Familiarity with distributed training, mixed-precision training, and accelerator hardware. - Strong written and verbal communication skills, including ability to explain complex methods clearly. - Demonstrated ability to read, evaluate, and adapt techniques from current research literature. - Track record of shipping impactful applied AI projects. Preferred Qualifications - Published research at top-tier AI/ML venues. - Experience with large language model training, fine-tuning, or evaluation. - Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures. - Exposure to responsible AI, model evaluation, and alignment practices. - Experience contributing to open-source ML projects.

View details: AI Research Engineer, Applied AI

Worldwide

$100K - $150K / year

Senior AI Research Scientist

Cerence Inc.

Cerence is the global industry leader in creating AI-powered user experiences for automotive and transportation.

AI Research Scientist9 days ago

Full Time Remote SeniorTeam 1,001-5,000Since 2019H1B No Sponsor

View details: Senior AI Research Scientist

• Finetune LLMs using RL and other SOTA methods • Create conversational workflows involving tool-calls and static knowledge • Work with agent teams to build business logic into conversation • Employ multimodals to enrich the conversational UX • Innovate and build novel methods for faster inference

United States

AI Research Engineer – Agentic Post-training

Tether.to

Bringing real world currency to the blockchain.

AI Research Scientist10 days ago

Full Time Remote SeniorTeam 11-50Since 2014H1B No Sponsor

View details: AI Research Engineer – Agentic Post-training

• Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve SOTA results. • Drive broad, cross-cutting model improvements, including factuality, instruction adherence, tool/function use, multi-agent coordination, and reasoning calibration. • Design and enhance large-scale post-training systems, including data pipelines, training workflows, evaluation frameworks, and benchmark infrastructure. • Develop rigorous evaluation suites and diagnostic tools to assess model readiness for deployment. • Strengthen feedback loops from real-world product usage, incorporating both explicit and implicit user signals into post-training. • Collaborate with tooling, product, and training teams to improve the usefulness, reliability, and agentic capabilities of frontier models. • Closely liaise with research, engineering and cross-functional teams to determine which integrations are production-ready for inclusion in major model releases.

Node.js

Worldwide

AI Research Engineer – Kernel & Inference Optimization

Tether.to

Bringing real world currency to the blockchain.

AI Research Scientist10 days ago

Full Time Remote SeniorTeam 11-50Since 2014H1B No Sponsor

View details: AI Research Engineer – Kernel & Inference Optimization

• Drive innovation in model serving and inference architectures for advanced AI systems. • Focus on optimizing model deployment and inference strategies. • Work on a wide spectrum of systems, from resource-efficient models to complex, multi-modal architectures. • Develop, test, and implement novel serving strategies and inference algorithms. • Engineer robust inference pipelines, establish performance metrics, and resolve bottlenecks in production environments. • Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value.

North America

Machine Learning Researcher

Gray Swan

Empowering the world to use AI safely and securely

AI Research Scientist11 days ago

Full Time Remote Mid LevelTeam 11-50Since 2023H1B No Sponsor

View details: Machine Learning Researcher

Role Description As a Machine Learning Researcher at Gray Swan AI, you will work at the boundary between research and production — developing new approaches to adversarial testing, model evaluation, and robust inference that directly inform how secure AI systems are built and deployed. AI security is not a solved problem. This role is fundamentally about research: - Designing experiments - Prototyping novel methods - Analyzing results empirically - Translating findings into real systems that withstand adversarial pressure You will collaborate closely with engineering and platform teams to see your ideas through from prototype to production impact. What You'll Do: - Design and develop novel ML approaches to adversarial testing, model evaluation, and robust inference. - Build and deploy ML models that meet real-world performance and scalability requirements. - Design experiments, analyze results empirically, and communicate findings through publications and internal research reports. - Develop and advance methodologies for controlling, monitoring, and analyzing ML models in production environments. - Translate research ideas into scalable AI systems deployed in real-world, adversarial settings. - Work closely with cross-functional teams to ensure research outcomes inform production systems. Qualifications - Bachelor's degree in Computer Science, Machine Learning, Engineering, or a related technical field. - A Master's or PhD in a relevant technical field is strongly preferred, especially with a focus on machine learning and AI safety. Requirements - Experience building and deploying ML models and systems. - Demonstrated expertise in designing, training, and deploying deep learning models, particularly with PyTorch. - Strong Python programming skills; C++ preferred. - Experience developing scalable ML pipelines and integrating with cloud infrastructure (AWS, GCP, or Azure). - ML research experience: building research prototype systems, designing experiments, empirical analysis of results, and communicating results via publications. Benefits - Competitive compensation package designed to reward impact and incentivize growth. - Salary: $183,000-$278,000 - Equity: Competitive equity package - 401k with up to 4% matching - 28 days annual leave (vacation + holidays) - Health, dental, and vision coverage - Catered lunches (Pittsburgh office) - Flexible work arrangements - Visa sponsorship available for exceptional candidates

AI/ML AI Observability/Monitoring PyTorch Python C++AWS GCP Azure

United States

$183K - $278K / year

Bright Vision Technologies

AI Research Engineer – Applied AI

AI Research Scientist13 days ago

View details: AI Research Engineer – Applied AI

• Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. • Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies. • Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. • Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. • Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools. • Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. • Optimize models for accuracy, latency, throughput, and cost based on production requirements. • Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality. • Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements. • Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions. • Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences. • Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. • Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. • Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities.

Python PyTorch

United States