Frontier Ux Engineer Jobs

As a member of our Frontier Tech Consultant team, you will play a critical role in advancing cutting-edge AI innovations by conducting high-impact experiments and ensuring seamless execution at the highest quality standards. Your work will directly contribute to Scale AI’s growth, shaping the future of artificial intelligence. In this role, you will be working on various types of projects, including but not limited to: research experiments, dataset generation, data quality improvements, and in-depth technical analysis. You will tackle complex, technical and operational challenges while collaborating closely with Scale’s ML research scientists and SPM team. The ideal candidate is analytical, detail-oriented, and results-driven, with strong problem-solving abilities and excellent communication skills. We are looking for someone who thrives in a fast-paced environment, is proactive in overcoming challenges, and is committed to delivering exceptional outcomes. If you are eager to contribute to the forefront of AI innovation, we encourage you to apply. You will be responsible for: Design and execute research experiments Build and evaluate frontier LLM datasets Develop training and testing material for frontier pipelines Improve quality of existing and new products Ideally you’d have: Strong machine learning knowledge, either by being in the final years of a ML PhD career or having already graduated Strong writing and verbal communication skills An action-oriented mindset that balances creative problem solving with the scrappiness to ultimately deliver results Analytical, planning, and process improvement capability Experience working in a fast-paced, entrepreneurial environment Technical skills including familiarity with Python, GPU, AWS, API, LLM, ML, and SQL Pay: $60-80/hr Commitment: This is a fully remote, US-based part-time (10-20 hours per week), on-going contract position staffed via HireArt. HireArt values diversity and is an Equal Opportunity E

About the Role The Speculative Decoding team at Together AI builds the draft models and algorithms that let large language models generate faster without changing their outputs. Our speculators run in production on frontier open models such as DeepSeek, Kimi and GLM. Our work sits at the intersection of model training, inference algorithms and serving systems, and we co-design all three to lower the cost and latency of generation. As a research intern, you will dive into open questions in speculative decoding: new drafter architectures and verification schemes, knowledge distillation and training recipes that align draft and target models, and drafters that adapt as traffic shifts. You will train and fine-tune draft models at scale on dedicated GPU clusters, and test your ideas against realistic serving workloads, including long-context and agentic traffic. Projects aim to answer important research questions in speculative decoding, enabling faster generation on frontier models, drafters that keep pace with changing workloads, and robust, reproducible evaluation under real traffic. Past Research Speculative decoding research by Together researchers, interns and academic collaborators includes: Speculative Speculative Decoding (ICLR 2026) When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (ICML 2026) Beat the Long Tail: Distribution-Aware Speculative Decoding for RL Training (MLSys 2026) Responsibilities Research and prototype new efficient speculative decoding algorithms and draft model architectures Train, fine-tune and distill draft models, and design rigorous experiments to validate hypotheses. Implement optimized version for serving engines to show efficacy in real world workloads Communicate the plans, progress, and results of projects to the broader team Share findings through technical write-ups and blog posts, and help bring successful ideas into production Requirements Currently pursuing a Master's or Ph.D. degree in Computer Sci

About the Role The Speculative Decoding team at Together AI builds the draft models and algorithms that let large language models generate faster without changing their outputs. Our speculators run in production on frontier open models such as DeepSeek, Kimi and GLM. Our work sits at the intersection of model training, inference algorithms and serving systems, and we co-design all three to lower the cost and latency of generation. As a research intern, you will dive into open questions in speculative decoding: new drafter architectures and verification schemes, knowledge distillation and training recipes that align draft and target models, and drafters that adapt as traffic shifts. You will train and fine-tune draft models at scale on dedicated GPU clusters, and test your ideas against realistic serving workloads, including long-context and agentic traffic. Projects aim to answer important research questions in speculative decoding, enabling faster generation on frontier models, drafters that keep pace with changing workloads, and robust, reproducible evaluation under real traffic. Past Research Speculative decoding research by Together researchers, interns and academic collaborators includes: Speculative Speculative Decoding (ICLR 2026) When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (ICML 2026) Beat the Long Tail: Distribution-Aware Speculative Decoding for RL Training (MLSys 2026) Responsibilities Research and prototype new efficient speculative decoding algorithms and draft model architectures Train, fine-tune and distill draft models, and design rigorous experiments to validate hypotheses. Implement optimized version for serving engines to show efficacy in real world workloads Communicate the plans, progress, and results of projects to the broader team Share findings through technical write-ups and blog posts, and help bring successful ideas into production Requirements Currently pursuing a Master's or Ph.D. degree in Computer Sci

As Scale continues to build and develop its field marketing and events program, we are looking for a Field Marketing & Events Manager to support our Frontier Data team in their event and field marketing initiatives. You’ll plan and run a wide range of events, from research conferences to Scale-hosted executive dinners and practitioner meetups, and support our annual flagship conference, the AI Leadership Summit. You will join a rapidly growing team with the opportunity to manage and execute events from start to finish, drive lead generation, pipeline growth, and brand awareness, and plan event programming with the largest names in AI. What you’ll do Execute end-to-end planning and logistics for executive dinners, meetups, happy hours, sponsored research conferences and trade shows, and virtual events. Design event programs that support sales goals, pipeline growth, and deal acceleration. Support event logistics for our annual flagship conference, the AI Leadership Summit. Manage event budgets and relationships with event production firms, contractors, and outside vendors. Partner with growth marketing on campaigns and MQL reporting. Work cross-functionally with sales and engineering to define event goals, shape content, and deliver against shared KPIs. Track event performance, measuring ROI and key metrics in Salesforce. Work with external executives and their teams to secure speakers and support their presentation planning. What you’ll bring 5+ years of hands-on experience in event marketing, including vendor management, booth logistics, staffing, and executive participation. Experience fully managing and executing events ranging from small, intimate executive-level gatherings to large conferences with 1,000+ attendees. Exceptional program management and multitasking skills, with the ability to influence and drive alignment across diverse internal teams and stakeholders. Impeccable attention to detail and a proactive approach to troubleshooting. Excellent writt

About the Role The Agents team investigates how to build, align, and scale frontier AI systems that can tackle complex, multi-step tasks and workflows across text and speech, with a particular focus on agentic and scientific domains. Our work sits at the intersection of agent capabilities, human-computer interaction, and infrastructure—from designing post-training methods for agentic behavior to developing evaluation frameworks for open-ended tasks where traditional metrics fall short. As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined. Projects may involve developing new training recipes for self-learning and long-horizon reasoning, curating datasets for non-deterministic scientific and agentic tasks, studying failure modes in agentic behavior, or building infrastructure that enables agent operations at scale. You'll operate in a space where algorithmic innovation, dataset and interaction design, and systems work come together to push the boundaries of what AI agents can reliably accomplish. Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Communicate the plans, progress, and results of projects to the broader team Requirements Currently pursuing a Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field Publications at leading ML, NLP, or speech conferences or journals (such as NeurIPS, ICML, ICLR, *ACL, EMNLP, Interspeech) Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Understanding of how LLMs work Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Example Research Directions Training, develo

About the Role The Agents team investigates how to build, align, and scale frontier AI systems that can tackle complex, multi-step tasks and workflows across text and speech, with a particular focus on agentic and scientific domains. Our work sits at the intersection of agent capabilities, human-computer interaction, and infrastructure—from designing post-training methods for agentic behavior to developing evaluation frameworks for open-ended tasks where traditional metrics fall short. As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined. Projects may involve developing new training recipes for self-learning and long-horizon reasoning, curating datasets for non-deterministic scientific and agentic tasks, studying failure modes in agentic behavior, or building infrastructure that enables agent operations at scale. You'll operate in a space where algorithmic innovation, dataset and interaction design, and systems work come together to push the boundaries of what AI agents can reliably accomplish. Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Communicate the plans, progress, and results of projects to the broader team Requirements Currently pursuing a Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field Publications at leading ML, NLP, or speech conferences or journals (such as NeurIPS, ICML, ICLR, *ACL, EMNLP, Interspeech) Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Understanding of how LLMs work Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Example Research Directions Training, develo

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake Frontier Engineeringは、Snowflakeプラットフォームの実装・戦略組織です。私たちはお客様のジャーニー全体を通じて、最適化・加速・ビジネス成果の実現を支援します。 私たちは、Snowflake Frontier Engineeringとお客様の間の重要な技術的橋渡し役を担うソリューションアーキテクトを求めています。このロールは、深いプラットフォーム専門知識とコンサルティング型セールスの能力を組み合わせ、プリセールスサイクルにおけるハンズオン技術リーダーシップを提供しながら、戦略的エンゲージメントにおける直接的な顧客デリバリーへの副次的な責任も担います。 理想的な候補者は、複雑な顧客のビジネス課題を、実現可能で説得力のあるSnowflakeソリューションに変換し、技術者および経営層の両方に自信を持ってそのビジョンを伝えることができます。 ソリューションアーキテクトとしてのあなたの役割 Frontier Engineering プラクティスマネージャーおよびプラクティスディレクターをサービスセールスサイクル全体にわたって支援する主要技術リソースとして機能します。 Snowflakeの顧客要件への対応能力を検証し、商談のクローズを加速させるための技術ディスカバリーセッション、ソリューションデザインワークショップ、アーキテクチャレビュー、概念実証（PoC）を主導します。 Snowflakeプラットフォームの機能に基づいた、デリバリー可能なソリューションの全体アーキテクチャを設計します。 作業範囲定義書（SOW）に直接反映されるソリューションアーキテクチャ、工数見積もり、リソース計画、リスク評価などの技術スコーピング成果物を作成します。 ディレクター、VP、CTO、CDOを含む顧客技術ステークホルダーとのトラステッドアドバイザー関係を構築します。 競争ポジショニング、技術的異論への対処、価値の明確化、拡張機会の特定を通じて、サービス案件の品質と受注率を向上させる技術的ソートリーダーシップを提供します。 主にソリューションアーキテクトが発案したエンゲージメント、短期のアーキテクチャ検証スプリント、または戦略的アカウントにおける経営層レベルのチェックポイントセッションへ選択的に参加します。 再利用可能なソリューションパターン、リファレンスアーキテクチャ、セールス資料、イネーブルメントコンテンツの作成に貢献します。 プラクティスマネージャーへの技術的ポジショニング指導、テクニカルアーキテクトへの顧客向けコミュニケーションスキル指導を行います。 顧客要件、競争ギャップ、新興市場ニーズについてプロダクトマネジメントおよびエンジニアリングに構造化されたフィードバックを提供します。 このロールは、フルタイムの実装ロールではありません。デリバリーへの関与は時間制限があり意図的であり、プラットフォームへの精通を維持しながらセールスサイクル中に構築した顧客関係を強化することを目的としています。 理想的なソリューションアーキテクト

Scale AI’s Public Sector business is growing quickly as government agencies adopt AI to support critical national security, defense, and public sector missions. We are seeking a hands-on Engineering Manager to lead our Frontier AI Infrastructure team within Public Sector Engineering. This is a 50/50 role : you'll spend roughly half your time writing and reviewing code, and half your time leading, coaching, and growing a small team of engineers. Your team owns the model inference layer — enabling state-of-the-art models, debugging the latest AI tools, managing networking, debugging latency, and tracking pricing/usage metrics for AI models. You'll lead technical discussions on the frontlines with cloud vendors and customers to deliver on critical contracts and debug platform issues, and you'll partner upstream with Product to catch issues before they break, moving the team from "infra-only debugging" to proactive integration testing. You will: As a manager - 50/50 management and hands on coding experience. Lead, coach, and grow a team of engineers — own hiring, onboarding, performance, and career development. Set technical direction and quarterly priorities for the inference layer, and hold the team accountable to delivery on customer and contract commitments. Run the operational rhythm of the team: sprint planning, on-call rotation, incident review, and postmortems. Partner with Product, Program, and Deployed Engineering leadership to translate customer and mission requirements into roadmap. Represent the team in customer engagements and vendor escalations, including with government stakeholders. Design and implement secure, scalable backend systems for Public Sector customers, leveraging Scale's modern cloud-native AI infrastructure. Own services and systems alongside your team, define their long-term health goals, and improve the health of surrounding components. Stay in the code: review PRs, debug production issues, and take on meaningful technical wor

Join the team shaping the future of AI at Scale. Scale builds RL environments: sandboxed replicas of the digital spheres where real knowledge work occurs and built from the operating data of the companies that actually hold it. As the Data Acquisition Lead , you will own the commercial motion that gets us that data end to end. You will figure out which companies sit on the data for the next domain worth owning and then go and get it. This is a zero-to-one function with no playbook. You should be prepared to wear many hats, from thesis-driven dealmaker to hands-on operator to technical translator between commercial and Research teams. You will: Map the supply side. Work backwards from where labs are pushing to the specific organizations holding the underlying data. Build a thesis on which domains are worth owning and in what order. Invent the deal structures. You'll work with Scale’s legal team to define the first version of how these transactions get priced. Close. Own it from cold outreach to signature. Close the loop with the technical side. You need to hold a real conversation about what makes a dataset trainable and become an expert in what makes this underlying data valuable. Build the machine. Do the work by hand first, then turn what you learn into a repeatable pipeline. Ideally, you’d have: 5+ years across some mix of business development, corp dev, commercial strategy, or early-stage GTM. The label matters less than a track record of building a commercial motion that didn't exist before you got there A strong track record of managing important external relationships Strong business judgment and the ability to evaluate partnership value quickly Clear communication skills and comfort working with senior stakeholders Ability to operate independently while staying closely connected to cross-functional teams A practical, hands-on approach to building new functions from the ground up Comfort working in fast-moving, ambiguous environments Experience in

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The GTM Strategy & Operations, Frontier team coordinates how Anthropic's research reaches the market: how we run model launches commercially, how we partner with Capacity teams to allocate a finite compute supply across customers and surfaces, and how we translate what the field learns into reads that Research, Product, Finance, and GTM leadership act on. One tangible example of the work this role will contribute to is new model and product introduction: the end-to-end commercial run of a model, product or capability release, from identifying market opportunities to preparing customer cohorts and the field through to the adoption readout that shapes where we go from there. We are hiring for someone who can run a high-stakes cross-functional program where the bar for execution is sky high, author the strategic analysis and readouts executive leadership needs, and create and optimize repeatable methodologies. This role reports to the Global Head of GTM Strategy & Operations, Frontier, and sits at the interface between GTM, Applied AI, Product, Research, Security & Safeguards, Finance, and Compute. Key responsibilities Run launches Serve as GTM DRI for product and model launches: own account selection for pre-launch, coordinate field readiness across scaled pre- and post-sales teams, and run the launch-week operating room with extreme ownership. Synthesize customer adoption signal (usage patterns, migration behavior, qualitative feedback from the field) into actionable reads for executive leadership across the company Design and run the field response loop: how AE, CS, Applied AI and customer respon

About the Team Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. Mitigating the frontier risks resulting from these capabilities is paramount to OpenAI’s ability to continue deploying models safely. The Preparedness team is dedicated to addressing these critical risks. Our work includes: Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. Mitigation. Keeping misuse and misalignment safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework , and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. About the Role We are seeking exceptional researchers who can push the frontier of safety mitigations. You will help derisk frontier models by developing novel safety mitigations, developing and applying new techniques from domains like interpretability, control, and alignment to ensure the safety of OpenAI’s deployed models. You will play a critical role in defining how a safe AI system should look in the future at OpenAI, making a significant impact on our mission to build and deploy safe AGI. This role requires strong technical depth and close cross-functional collaboration to ensure our safety mitigations are enforceable, scalable, and effective. We seek researchers who can partner with experts across domains such as misalignment, cybersecurity, and biology in order to develop the best possible end-to-end safety stack. In this role, you will: Work on identifying emerging AI safety risks and new methodologies for exploring and mitigating the impact of such risks Build (and then continuously refine) the evaluations that enable us to assess the extent of these risks; this might include worki

About the team Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. Mitigating the frontier risks resulting from these capabilities is paramount to OpenAI’s ability to continue deploying models safely. The Preparedness team is dedicated to addressing these critical risks. Our work includes: Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. Mitigation. Keeping misuse and misalignment safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework , and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. About the role As we push toward AGI, cybersecurity becomes one of the most important and urgent frontiers: the same systems that can accelerate productivity can also accelerate exploitation. As a Researcher for cybersecurity risks, you will help design and implement an end-to-end mitigation stack to reduce severe cyber misuse across OpenAI’s products. This role requires strong technical depth and close cross-functional collaboration to ensure safeguards are enforceable, scalable, and effective. You’ll contribute directly to building protections that remain robust as products, model capabilities, and attacker behaviors evolve. In this role, you will: Design and implement mitigation components for model-enabled cybersecurity misuse—spanning prevention, monitoring, detection, and enforcement—under the guidance of senior technical and risk leadership. Integrate safeguards across product surfaces in partnership with product and engineering teams, helping ensure protections are consistent, low-latency, and scale with usage and new model capabilities. Evaluate technical trade-offs within the cybers

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval , SWE-bench Verified , MLE-bench , PaperBench , and SWE-Lancer . If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you might Create ambitious RL environments to push our models to their limits, and measure frontie

About the team The Computer Use and New Interfaces team is focused on discovering and building the next generation of AI-native interfaces. We believe that the value of AI is increasingly constrained not by model capabilities, but by the ways people interact with those capabilities. Our mission is to create new interaction paradigms that unlock the full potential of AI and integrate it more deeply into people's lives and work. Our team does both near-term product development and longer-term product incubation that can influence experiences across ChatGPT, Codex, future OpenAI products, and emerging device platforms. We work in a highly collaborative, design-driven environment where engineering, product, and design operate as one team. We value rapid experimentation, prototyping, and iteration, creating the shortest possible path between an idea, a working system, and a product decision. About the role We're looking for exceptional engineers who are excited to invent entirely new ways for people to interact with AI. You'll work at the intersection of engineering, product, and design to explore, prototype, and build novel interface concepts that push beyond traditional software paradigms. This role requires comfort with ambiguity, strong product instincts, and a willingness to move fluidly between experimentation and production systems. You'll help shape both the capabilities and the user experiences that define how people engage with AI. In this role you will: Design, prototype, and build novel AI-native interfaces and interaction models. Develop foundational technologies and frameworks that enable generative UI and computer use experiences. Collaborate closely with designers, product thinkers, and engineers to rapidly explore and validate new concepts. Build end-to-end prototypes and production systems that can influence future OpenAI products. Contribute to platform technologies that can be adopted across multiple product surfaces. Help establish technical directio

About the Team Our Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. Within Safety Systems, the Model Policy team aligns model behavior with desired human values and norms. We co-design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety. About the Role Frontier AI systems are rapidly expanding what is possible in cybersecurity and software engineering. These capabilities create major defensive opportunities, but they also raise serious dual-use and misuse risks across areas such as malware development, exploit discovery, vulnerability chaining, credential abuse, cyber intrusion, and autonomous offensive operations. In this role, you will help define how OpenAI’s models should behave in high-risk cybersecurity contexts. You will develop policy frameworks, threat models, taxonomies, evaluations, and behavioral specifications that guide model behavior across training, deployment, and monitoring systems. This role sits at the intersection of cybersecurity, AI safety, threat modeling, evaluation science, and policy implementation. You will work closely with research, engineering, safety training, preparedness, and product teams to build policies that are technically grounded, measurable, enforceable, and responsive to real-world cyber risk. Your Responsibilities: Design and maintain model policies for cybersecurity and frontier-risk domains, especially dual-use and high-risk cyber capabilities. Translate cybersecurity threat models into clear behavioral specifications, evaluation criteria, grading guidance, and system-level mitigations. Define practical boundaries between legitimate security research, defensive workflows, and assistance that could materially enable harmful activity. Build policy artifacts that support i