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software engineer, ai product Jobs in San Francisco

software engineer, ai product

2026 phd software engineer intern (machine learning), united states Jobs in San Francisco

2026 phd software engineer intern (machine learning), united states

full stack software engineer Jobs in San Francisco

full stack software engineer

principal software engineer - developer products (ai) Jobs in San Francisco

principal software engineer - developer products (ai)

staff machine learning engineer

Quizlet

<div><div><b>About Quizlet:</b></div><div></div><div><br>
At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ learning platform serves tens of millions of students every month, including two-thirds of U.S. high schoolers and half of U.S. college students, powering over 2 billion learning interactions monthly.</div><div></div><div><br>
We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We&rsquo;re energized by the potential to power more learners through multiple approaches and various tools.</div><div></div><div><b><br>
Let&rsquo;s Build the Future of Learning</b></div><div>
Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.</div><div></div><div><br>
About the Team:</div><div></div><div><br>
The Personalization &amp; Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities and experiences that best fit their goals. We power recommendation and search systems across multiple surfaces, from home feed and search results to adaptive study modes.</div><div></div><div><br>
Our mission is to make Quizlet feel uniquely tailored for every learner by combining cutting-edge machine learning, scalable infrastructure and insights from learning science.</div><div></div><div><br>
You&rsquo;ll collaborate closely with product managers, data scientists, platform engineers and fellow ML engineers to deliver personalized learning pathways that drive engagement, satisfaction and measurable learning outcomes.</div><div></div><div><br>
About the Role:</div><div></div><div><br>
As a Senior or Staff Machine Learning Engineer on the Personalization &amp; Recommendations team, you&rsquo;ll design and build large-scale retrieval, ranking, and recommendation systems that directly shape how learners discover and engage with Quizlet.</div><div></div><div><br>
You&rsquo;ll bring deep expertise in modern recommender systems &mdash; from deep learning&ndash;based retrieval and embeddings to multi-task ranking and evaluation &mdash; and help evolve Quizlet&rsquo;s personalization stack to power adaptive, effective learning experiences.</div><div></div><div><br>
You&rsquo;ll work at the intersection of machine learning, product design, and scalable systems, ensuring our recommendations are performant, responsible, and aligned with learner outcomes, privacy, and fairness.</div><div></div><div><br>
We&rsquo;re happy to share that this is an <b>onsite position</b> in our San Francisco office. To help foster team collaboration, we require that employees be in the office <b>a minimum of three days per week</b>: Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe that this working environment.</div>
<h3 class="jobSectionHeader"><b>In this role, you will:</b></h3><ul><li>
Design and implement personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals, and content features</li><li>
Develop scalable retrieval and serving systems using architectures such as Two-Tower, deep ranking, and ANN-based vector search for real-time personalization across surfaces</li><li>
Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training&ndash;serving consistency, and robust monitoring</li><li>
Partner closely with Product and Data Science to translate learner objectives (engagement, retention, mastery) into measurable modeling goals and experimentation plans</li><li>
Advance evaluation methodologies, refining offline metrics (e.g., NDCG, CTR, calibration) and supporting rigorous A/B testing to measure learner and business impact</li><li>
Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and serving cost at scale</li><li>
Contribute to the long-term technical vision for personalization and recommendations, aligning modeling strategy with Quizlet&rsquo;s AI and product roadmaps</li><li>
Stay current with RecSys research and industry trends, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into production</li><li>
Mentor other engineers and applied scientists, fostering technical growth, experimentation rigor, and responsible ML practices</li><li>
Champion collaboration, inclusion, and curiosity, helping build a team culture that values diverse perspectives and data-driven problem-solving</li></ul><h3 class="jobSectionHeader"><b>
What you bring to the table:</b></h3><ul><li>
10+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems</li><li>
Track record of measurable impact, improving key online metrics such as CTR, retention, or engagement through recommender or search systems in production</li><li>
Strong hands-on skills in Python and PyTorch, with expertise in data and feature engineering, distributed training and inference on GPUs, and familiarity with modern MLOps practices &mdash; including model registries, feature stores, monitoring, and drift detection</li><li>
Deep understanding of retrieval and ranking architectures, including Two-Tower models, deep cross networks, Transformers, or MMoE, and how to apply them in production contexts</li><li>
Experience with large-scale embedding models and vector search (e.g., FAISS, ScaNN), including training, serving, and optimization at scale</li><li>
Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test results to drive product decisions</li><li>
Ability to communicate complex technical ideas clearly, collaborating effectively with product managers, data scientists, and engineers across teams</li><li>
Growth and mentorship mindset, contributing to team learning and helping raise the bar for modeling quality, experimentation, and reliability</li><li>
Commitment to responsible and inclusive personalization, ensuring our ML systems respect learner privacy, fairness, and diverse goals</li></ul><h3 class="jobSectionHeader"><b>
Bonus points if you have:</b></h3><ul><li>
Publications or open-source contributions in RecSys, search, or ranking</li><li>
Familiarity with reinforcement learning for recommendations or contextual bandits</li><li>
Experience with hybrid RecSys systems blending collaborative filtering, content understanding, and LLM-based reasoning</li><li>
Prior work in consumer or EdTech applications with personalization at scale</li></ul><h3 class="jobSectionHeader"><b>
Compensation, Benefits &amp; Perks:</b></h3><ul><li>
Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $209,920 - $285,000, depending on location and experience, as well as company stock options</li><li>
Collaborate with your manager and team to create a healthy work-life balance</li><li>
20 vacation days that we expect you to take!</li><li>
Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)</li><li>
Employer-sponsored 401k plan with company match</li><li>
Access to LinkedIn Learning and other resources to support professional growth</li><li>
Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits</li><li>
40 hours of annual paid time off to participate in volunteer programs of choice</li></ul><div><b>
Why Join Quizlet?</b></div><div></div><div><br>
 \uD83C\uDF0E Massive reach: 60M+ users, 1B+ interactions per week</div><div>
 \uD83E\uDDE0 Cutting-edge tech: Generative AI, adaptive learning, cognitive science</div><div>
 \uD83D\uDCC8 Strong momentum: Top-tier investors, sustainable business, real traction</div><div>
 \uD83C\uDFAF Mission-first: Work that makes a difference in people&rsquo;s lives</div><div>
 \uD83E\uDD1D Inclusive culture: Committed to equity, diversity, and belonging</div><div></div><div><b><i><br>
We strive to make everyone feel comfortable and welcome!</i></b></div><div>
We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership.</div><div></div><div><br>
We provide a transparent setting that gives a comprehensive view of who we are!<br>
</div><div></div><br>
<div><b>In Closing:</b></div><div></div><div><br>
At Quizlet, we&rsquo;re excited about passionate people joining our team&mdash;even if you don&rsquo;t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high-quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.&rdquo;</div><div></div><div><br>
Quizlet&rsquo;s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion.</div><div></div><div><br>
As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us!</div><div></div><div><b><br>
To All Recruiters and Placement Agencies:</b></div><div></div><div><br>
At this time, Quizlet does not accept unsolicited agency resumes and/or profiles.</div><div>
Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet.</div><div>
#LI-FT</div>
<p>
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.</p></div>

Staff Machine Learning Engineer

Samsara

<div><div><p><b>Who we are</b></p>
<p>
Samsara (NYSE: IOT) is the pioneer of the Connected Operations&trade; Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing &mdash; and we are excited to help digitally transform their operations at scale.</p>
<p>
Working at Samsara means you'll help define the future of physical operations and be on a team that's shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you'll have the autonomy and support to make an impact as we build for the long term.</p></div><p><b>
About the role:</b></p>
<p>
The Samsara AI team builds end-to-end AI solutions for our customers as well as core ML infrastructure for Samsara. As a Staff Machine Learning Engineer, you will be working with petabyte-scale sensor, diagnostic, video, and text data to solve critical problems for Physical Operations customers, globally. You will work closely with ML Engineers and Scientists, as well as full-stack and firmware engineers to deliver core product features, services, and optimizations.</p>
<p><b>
This is a remote position open to candidates residing in the United States. This position requires travel up to 5% of the time. Relocation assistance will not be provided for this role.</b></p>
<p><b>
You should apply if:</b></p>
<ul>
<li><b>You want to impact the industries that run our world:</b> The software, firmware, and hardware you build will result in real-world impact&mdash;helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.</li>
<li><b>You want to build for scale:</b> With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world's physical operations.</li>
<li><b>You are a life-long learner:</b> We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.</li>
<li><b>You believe customers are more than a number: </b>Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.</li>
<li><b>You are a team player:</b> Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we're all racing toward our connected operations vision, and we intend to win&mdash;together.</li>
</ul><h4 class="jobSectionHeader"><b>In this role, you will:</b></h4>
<ul><li>Lead design and implementation of critical AI product initiatives on Edge devices.</li>
<li>Develop both tactical AI solutions as well as more strategic and longer term research.</li>
<li>Work with petabyte-scale data from customer operations including text, transactions, diagnostics, sensor, camera, and location data.</li>
<li>Partner across business units to explore and prototype new AI experiences and optimize the ML model performance on edge devices.</li>
<li>Stay connected to industry and academic research and adopt novel technology that suits Samsara's needs.</li>
<li>Champion, role model, and embed Samsara's cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices</li>
</ul>
<p><b>Minimum requirements for the role:</b></p>
<ul>
<li>8+ years experience as a Machine Learning Engineer.</li>
<li>Profound experience in optimizing ML models and systems for Edge compute constraints.</li>
<li>Proficient with Spark, Ray, or a similar framework.</li>
<li>Coding in python or similar.</li>
<li>Coding in C++ or Rust.</li>
<li>Strong functional knowledge of the iterative machine learning product development process.</li>
<li>Experienced in developing and shipping production code at large scale.</li>
<li>Ability to distill informal or ambiguous customer and business requirements into crisp problem definitions.</li>
<li>Proven ability to communicate verbally and in writing to technical peers and leadership teams with various levels of technical knowledge.</li>
<li>Experience coaching and mentoring ML Engineers.</li>
</ul>
<p><b>An ideal candidate also has:</b></p>
<ul>
<li>Proficiency in self-serving with data for experiments and model training at scale.</li>
<li>An established record of successful high impact deliveries in AI.</li>
<li>Deep knowledge in state of the art Computer Vision and multi-model models.</li>
</ul><div><p>
At Samsara, we welcome everyone regardless of their background. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender, gender identity, sexual orientation, protected veteran status, disability, age, and other characteristics protected by law. We depend on the unique approaches of our team members to help us solve complex problems and want to ensure that Samsara is a place where people from all backgrounds can make an impact.</p>
<p><b>
Benefits</b></p>
<p>
Full time employees receive a competitive total compensation package along with employee-led remote and flexible working, health benefits, and much, much more. Take a look at our Benefits site to learn more.</p>
<p><b>
Accommodations</b></p>
<p>
Samsara is an inclusive work environment, and we are committed to ensuring equal opportunity in employment for qualified persons with disabilities. Please email accessibleinterviewing@samsara.com or click here if you require any reasonable accommodations throughout the recruiting process.</p>
<p><b>
Flexible Working</b></p>
<p>
At Samsara, we embrace a flexible working model that caters to the diverse needs of our teams. Our offices are open for those who prefer to work in-person and we also support remote work where it aligns with our operational requirements. For certain positions, being close to one of our offices or within a specific geographic area is important to facilitate collaboration, access to resources, or alignment with our service regions. In these cases, the job description will clearly indicate any working location requirements. Our goal is to ensure that all members of our team can contribute effectively, whether they are working on-site, in a hybrid model, or fully remotely. All offers of employment are contingent upon an individual's ability to secure and maintain the legal right to work at the company and in the specified work location, if applicable.</p>
<p><b>
Fraudulent Employment Offers</b></p>
<p>
Samsara is aware of scams involving fake job interviews and offers. Please know we do not charge fees to applicants at any stage of the hiring process. Official communication about your application will only come from emails ending in '@samsara.com' or '@us-greenhouse-mail.io'. For more information regarding fraudulent employment offers, please visit our blog post here.</p></div></div>

Uber

<div>We're looking for PhD candidates in the machine learning and optimization domain to intern with a variety of teams during summer 2026 (12 weeks). You will be embedded in an engineering team and work closely with other specialists, data scientists, and product managers. As a PhD intern, you will work on an exciting yet bold problem independently, under the supervision of an experienced engineer on that team.
<br>
<br>
<b>About the Role
</b><br>
<br>
At Uber, we work on many ambitious engineering products covering many lines of business as well as the underlying platform technologies that power those businesses. We foster growth and increase profitability of Uber by pushing the frontiers of machine learning, constrained optimization, statistics, data science and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry.
<br>
<br>
As a PhD software engineer intern, you will have a lot of opportunities to work with product managers, data scientists and, of course, engineers from different teams. You will have an opportunity to learn how to iterate over a product for greater success while demonstrating your area of expertise (machine learning, statistics, constrained optimization, distributed system, etc.). This is a unique opportunity to grow your skills with real-world experience and do highly impactful, yet fun work at the same time. It is an ambitious yet rewarding job!
<br>
<br>
<b>Team Options
</b><ul><li>The <b>Delivery Marketplace: Consumer and Courier Pricing, Matching and Logistics </b>is responsible for building the core technology across the delivery line of business including food delivery, grocery, and retail last mile. These include a diverse set of transformational projects and impactful products ranging from finding the optimal match between jobs and earners in the delivery space, to setting optimal prices for Eaters and Couriers. The organization is formed of talented teams of engineers and scientists working side by side on the state of the art innovations on the matching objective function, pricing algorithms, and promotions. To achieve these goals, we leverage big data tools and apply advanced machine learning models to drive top line and bottom line business outcomes.
</li><li>The <b>Trusted Identity org </b>inspires trust in the Uber business cycle through verified and authentic identities. The Account Integrity team prevents fake identities from operating on the platform, and ensures that accounts are not duplicated nor fraudulent to enhance accountability. The Identity Verification team ensures accounts are created by real humans by verifying their attributes. The Account Security team is responsible for defending account holders from fraud related to phishing and ATO (account takeover) attempts. The Device Intelligence team is responsible for collecting device signals that are critical for the other teams to reliably achieve their goals.
</li><li>The Uber <b>Risk engineering team </b>is tackling challenges related to fraud and abuse detection. Our work is crucial in maintaining the integrity of Uber's platform. The team employs advanced machine learning including LLMs and computer vision, knowledge graph, similarity search and reinforcement learning to monitor transactions, detect unusual patterns, and implement robust verification methods
</li><li>The <b>Mobility Matching team </b>at Uber plays a crucial role in developing and optimizing algorithms and systems that match supply (drivers) with demand (riders) in real-time. You will work on complex problems, leveraging data and build ML algorithms to ensure efficient and reliable marketplace matching. The team is broadly part of the Marketplace (PIMS) org, a central pillar to Uber's core technology which includes pricing, incentives/investments, matching, surge, etc. As the key brain of the company, we manage the complex dynamics of supply and demand, optimize dispatching algorithms, and continuously innovate to enhance the overall user experience for both riders and drivers. Improvements in these systems increase revenue in the hundreds of millions of dollars, and decrease wasted time of drivers and users. Your contributions will directly impact the experience of millions of users worldwide.
</li><li>The <b>Rider Pricing and Incentives team </b>at Uber plays a crucial role in developing and optimizing algorithms and systems that determine the trip prices, discounting and promotions for Uber riders worldwide. You will work on complex modeling, pricing and incentives targeting problems, leveraging data and building ML algorithms to ensure accurate and robust pricing models and successful pricing optimization outcomes. The team is broadly part of the Marketplace organization, a central pillar to Uber's core technology which more broadly includes pricing, incentives/investments, matching, surge, etc. As a key team within Uber, we manage the complex dynamics between short term optimized pricing and long-term optimized pricing, promotion targeting and continuously innovating to enhance the overall user experience for riders. Improvements in these systems increase revenue in the hundreds of millions of dollars, and positively impact the experience of millions of users worldwide, as well as their ability to get around in the world.
</li><li>Join our <b>Capacity Planning team </b>as a Ph.D. intern to drive the future of infrastructure resource management through advanced quantitative methods. You will be at the forefront of implementing our data-driven forecasting vision, moving away from reactive, manual processes to scalable, automated solutions that utilize predictive analytics and real-time utilization data. This is a high-impact opportunity to work with the Applied Science and Engineering teams on designing and optimizing the central intelligence systems for capacity planning. Your expertise will be crucial in developing granular, highly accurate resource forecasts, ensuring service reliability for unexpected demand, and making key decisions that directly influence financial accountability, operational agility, and optimal resource utilization across the entire platform.
</li><li>The <b>Autonomous Vehicles Cloud team </b>is dedicated to accelerating the autonomous vehicles industry to L4 by serving as a premier AV data leader. Recently, we announced a partnership with NVIDIA to jointly develop a data factory for autonomous vehicle development. The team includes engineers with diverse backgrounds in embedded software, cloud computing, batch data processing, and ML research. We are looking for talent passionate about autonomous vehicles and eager to make a significant impact.
</li><li>The <b>Marketplace Signals </b>team at Uber is responsible for building and optimizing foundational marketplace signals that power user experiences and drive marketplace efficiency. Our team ensures that key signals-such as eyeball ETA, spinner time, and supply reliability indicators-are leveraged effectively across various Uber products and levers, enabling data-driven decision-making and seamless coordination across different business functions.
</li><li><b>Applied AI </b>is a horizontal AI team at Uber partnering with product and platform teams across the company to deliver cutting-edge machine learning solutions for core business problems. The Computer Vision team in AppliedAI specializes in Generative AI, Foundation Model, and classical Computer Vision solutions, and the ML infrastructure needed to scale these systems in production.
</li><li><b>Fabric Controller </b>brings together platforms that keep large scale systems resilient and easy to operate, including Cadence and Repair Engine. Cadence is an open source workflow orchestration engine that manages distributed state, retries, scaling, and recovery so teams can focus on business logic while supporting mission critical workloads across many industries. Repair Engine detects, diagnoses, and remediates issues by correlating signals across infrastructure and application layers through an ingestion pipeline and statistical analysis. An ML engine is being developed to improve diagnostics by learning patterns from historic issues, reducing false positives, and incorporating broader context such as workload types and traffic trends. Together these systems strengthen reliability, automation, and operational efficiency across the stack.
</li></ul><b>What You'll Do
</b><ul><li>Drive exciting, ambitious, previously unsolved projects from end to end
</li><li>Develop novel algorithms that use Uber data at global scale
</li><li>Thrive in an environment with ambiguous product requirements
</li><li>Collaborate closely with product managers and data scientists
</li><li>Be motivated to independently own and drive projects forward
</li><li>Have a passion to make Uber better for our customers
</li></ul><b>Basic Qualifications
</b><ul><li>Currently enrolled in a Ph.D. program studying computer science, machine learning, data mining, artificial intelligence, constrained optimization, statistics, or a related quantitative field
</li><li>Candidates must have at least one semester/quarter of their education left following the internship
</li><li>Knowledge of underlying technical foundations of statistics, machine learning, optimization, systems, etc.
</li><li>Experience in one or more object-oriented languages, including C++, Java, Python, or Go
</li></ul><b>Preferred Qualifications
</b><ul><li>Ability to communicate effectively with both technical and business partners
</li><li>Experience in simplifying/converting business problems into technical problems
</li>Research mentality with a bias towards action to structure a project from idea to experimentation to prototype to implementation<br>
<li>
</li></ul><br>
<br>
For New York, NY-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For San Francisco, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For Seattle, WA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For Sunnyvale, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For all US locations, you will also be eligible for various benefits.</div>

2026 PhD Software Engineer Intern (Machine Learning), United States

Aldea Inc

<div><p><b>About Aldea</b></p><p>
Aldea is a multi-modal foundational AI company reimagining the scaling laws of intelligence. We believe today's architectures create unnecessary bottlenecks for the evolution of software. Our mission is to build the next generation of foundational models that power a more expressive, contextual, and intelligent human&ndash;machine interface.</p><p></p><p><b><br>
The Role</b></p><p>
We are hiring a Research Engineer (Machine Learning) to build the infrastructure that powers Aldea's multi-modal AI research. You will design, optimize, and scale the training and inference systems that enable our research team to explore next-generation architectures across language, speech, and multi-modal domains.</p><p></p><p><br>
This is a high-leverage role where your work directly enables breakthrough research. You'll build production-grade systems supporting rapid experimentation at billion-parameter scale and real-time deployment of speech and language models. If you're passionate about building the systems that accelerate AI research, this role is for you.</p><p></p><p><b><br>
What You'll Do</b></p><ul><li>
Build and maintain distributed training infrastructure supporting researchers across language and speech domains at a billion-plus-parameter scale.</li><li>
Optimize training and inference performance across the stack, delivering significant speedups through framework optimization, custom kernels, and system-level improvements.</li><li>
Design experiment infrastructure including automated evaluation pipelines, experiment tracking, and monitoring systems that enable rapid iteration.</li><li>
Scale infrastructure from single-node to multi-node distributed training and deploy production inference systems for real-time applications.</li><li>
Support researchers with fast turnaround on infrastructure issues and maintain high reliability across all systems.</li><li>
Collaborate with research scientists, data engineers, and leadership to define technical priorities and infrastructure roadmap.</li></ul><p><b>
Minimum Qualifications</b></p><ul><li>
Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience.</li><li>
3+ years of experience with PyTorch and distributed training frameworks (DDP, FSDP, DeepSpeed, or similar).</li><li>
Experience training large-scale deep learning models at 1B+ parameters.</li><li>
Deep understanding of training optimization techniques including mixed precision, gradient checkpointing, and memory management.</li><li>
Proven ability to build production-grade ML infrastructure with high reliability.</li><li>
Track record of delivering significant performance optimizations in ML training or inference systems.</li></ul><p><b>
Preferred Qualifications</b></p><ul><li>
Experience with custom kernel development (CUDA, Triton) or GPU optimization.</li><li>
Hands-on experience with large-scale pretraining (100B+ tokens, ideally trillion+ scale).</li><li>
Experience optimizing inference for production: quantization, vLLM, TensorRT, or custom serving engines.</li><li>
Familiarity with speech/audio ML systems and real-time inference constraints.</li><li>
Experience building automated evaluation frameworks and experiment tracking systems.</li><li>
Knowledge of profiling tools and multi-node training across 8-32+ GPUs.</li><li>
Exposure to job orchestration systems (SLURM, Kubernetes, Ray).</li><li>
Master's or PhD in Computer Science, Machine Learning, or related field.</li></ul><p><b>
Compensation &amp; Benefits</b></p><ul><li>
Competitive base salary</li><li>
Performance-based bonus aligned with research and model milestones</li><li>
Equity participation</li><li>
Comprehensive health, dental, and vision coverage</li><li>
Flexible paid time off</li></ul><p></p><p><i><br>
Aldea is proud to be an equal-opportunity employer. We are committed to building a diverse and inclusive culture that celebrates authenticity to win as one. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, citizenship or immigration status, or any other legally protected characteristics.</i></p><p></p><p><i><br>
Aldea uses E-Verify to confirm employment eligibility in compliance with federal law. For more information please visit: </i>https://www.e-verify.gov<i>.</i></p><p></p><p><i><br>
Please note: We do not accept unsolicited resumes from recruiters or employment agencies and will not be responsible for any fees related to unsolicited resumes.</i></p></div>

Research Engineer (Machine Learning)

Delty (YC X25)

<div>
 <strong>
  About Us
  <br/>
  <br/>
 </strong>
 <strong>
  Delty is building the world&rsquo;s first &ldquo;AI Staff Engineer.&rdquo;
 </strong>
 Unlike typical code-generation tools, Delty is trained on a team&rsquo;s codebase, documentation, and system history &mdash; giving it a system-level understanding of architecture, conventions, and constraints. Delty helps engineering teams design enterprise-scale software systems, make architectural decisions, and enable AI coding agents to work with real system context.
 <br/>
 <br/>
 Delty was founded by former engineering leaders from
 <strong>
  Google, including co-founders with deep experience at YouTube and in large-scale infrastructure
 </strong>
 . You&rsquo;ll get to work alongside people who built massive systems at scale &mdash; a chance to learn a
 <em>
  lot
 </em>
 and contribute meaningfully from day one.
 <br/>
 <br/>
 We believe in solving hard problems together as a team, iterating quickly, and building software with long-term thinking and ownership.
 <br/>
 <br/>
 What You&rsquo;ll Do
 <br/>
 <br/>
 <ul>
  <li>
   Work full-stack: design and build features spanning front-end, back-end, data storage and processing.
  </li>
  <li>
   Build new product modules and services from scratch &mdash; or evolve existing ones &mdash; guided by context-aware system design.
  </li>
  <li>
   Work with AI and machine learning: integrate large-language models (LLMs), process large or long-form text data, apply traditional ML (e.g. regression, data pipelines), and build tooling around AI-driven flows.
  </li>
  <li>
   Make architectural decisions &mdash; choose frameworks, data models, APIs, storage solutions &mdash; balancing trade-offs between performance, scalability, maintainability, and complexity.
  </li>
  <li>
   Collaborate closely with co-founders and other engineers to translate product vision into a working, maintainable codebase.
   <br/>
   <br/>
  </li>
 </ul>
 What We&rsquo;re Looking For
 <br/>
 <br/>
 <ul>
  <li>
   At least 3 years of full-stack engineering experience, including substantial work with AI/ML.
  </li>
  <li>
   Strong skills across front-end, back-end, databases/data storage &mdash; and demonstrated ability to design end-to-end systems.
  </li>
  <li>
   Experience working with or integrating AI/ML &mdash; LLMs, data pipelines, long-form text processing, traditional ML like regression or statistical modeling.
  </li>
  <li>
   Good design sense and architectural thinking: you understand trade-offs (scalability vs complexity, speed vs maintainability) and can choose wisely based on constraints.
  </li>
  <li>
   Comfort working in a fast-paced startup-style environment: nimble, iterative, high ownership.
  </li>
  <li>
   Bonus: prior startup experience, or even having been a founder &mdash; we value entrepreneurial thinking, self-direction, and willingness to wear multiple hats.
   <br/>
   <br/>
  </li>
 </ul>
 Why join
 <br/>
 <br/>
 <ul>
  <li>
   Learn from seasoned Google engineers: As former Google engineers who built systems at YouTube and Google Pay, we&rsquo;ve operated at massive scale. Working alongside us gives you a chance to build similar systems and learn best practices, scale thinking, and software design deeply.
  </li>
  <li>
   High impact: At a small but ambitious team, your contributions will influence architecture, product direction, and core features. You will have real ownership and see the effects of your work quickly.
  </li>
  <li>
   Grow fast: We&rsquo;re iterating rapidly; you&rsquo;ll be exposed to the full stack, AI/ML pipelines, system architecture, data modeling, and product-level decisions &mdash; a fast-track to becoming a senior engineer or technical lead.
  </li>
  <li>
   Challenging and meaningful work: We&rsquo;re tackling the hardest part of software engineering: bridging AI-generated prototypes and robust, scalable enterprise-grade systems. If you enjoy thinking deeply about systems and building reliable, maintainable foundations &mdash; this is for you.
  </li>
 </ul>
</div>


Full stack software engineer

Skyfire

<div><p><b>About Skyfire</b><br>
Skyfire is building the payment backbone for the emerging machine economy &mdash; a future where AI and machines transact seamlessly, instantly, and autonomously. Current financial systems aren&rsquo;t designed for this, so we&rsquo;re creating one that is.By leveraging stablecoins and blockchain technology, Skyfire enables instant, global micropayments between AI, APIs, data providers, and services. Enterprises can also automate microtransactions at scale &mdash; all with speed, security, and transparency. Our team brings deep experience in payments, fintech, and distributed systems &mdash; and we&rsquo;re moving fast to define how the machine economy will pay for itself.</p><h2 class="jobSectionHeader"><b>
Why This Role Matters</b></h2><p>
As a <b>Backend Engineer</b> at Skyfire, you&rsquo;ll be building the infrastructure that powers high-speed, high-volume machine-to-machine payments. From smart contract integrations to high-throughput APIs, you&rsquo;ll architect, code, and optimize backend systems that will scale from tens to thousands of requests per second. Your work will be critical in making AI-to-AI payments not just possible, but frictionless.</p><h2 class="jobSectionHeader"><b>
In This Role, You&rsquo;ll Get To:</b></h2><ul><li><b>
Design and develop</b> core backend components, including smart contract integrations, payment processing logic, and APIs.<br>
</li><li><b>
Implement scalable blockchain solutions</b> for on-chain machine payments with high throughput and low latency.<br>
</li><li><b>
Collaborate closely with product</b> to translate requirements into robust, maintainable technical solutions.<br>
</li><li><b>
Optimize performance</b> for speed, scalability, and cost-efficiency across distributed systems.<br>
</li><li><b>
Mentor junior engineers</b> and foster a culture of technical excellence and innovation.<br>
</li><li><b>
Stay ahead of the curve</b> on blockchain, smart contracts, and AI technologies &mdash; integrating them where they create real impact.<br>
</li></ul><h2 class="jobSectionHeader"><b>
We&rsquo;re Excited About You Because You Have:</b></h2><ul><li>
A <b>self-starter mindset</b> and the drive to tackle big challenges head-on.<br>
</li><li>
Strong <b>TypeScript proficiency</b> and <b>Node.js</b> backend development expertise.<br>
</li><li>
Experience with <b>PostgreSQL or MySQL</b> and sound database design skills.<br>
</li><li>
Solid <b>system design skills</b> to scale from 10s to 1000s of RPS.<br>
</li><li>
Familiarity with <b>CI/CD, Kubernetes, and Docker</b> for smooth build and deployment.<br>
</li><li>
Experience with <b>cloud infrastructure</b> (AWS, GCP, or Azure) &mdash; including networking fundamentals like load balancers and firewalls.<br>
</li><li>
Strong skills in <b>testing, debugging, and reliability engineering</b>.</li></ul><h2 class="jobSectionHeader"><b>
Bonus Points If You Have:</b></h2><ul><li>
Experience with <b>REST or GraphQL APIs</b> (design + integration).<br>
</li><li>
Knowledge of <b>OAuth 2.0, OpenID Connect</b>, SSO, and RBAC.<br>
</li><li>
Understanding of <b>time complexity</b> and system performance optimization.<br>
</li><li>
Familiarity with <b>NoSQL / Key-Value Stores</b>.<br>
</li><li>
Awareness of <b>security best practices</b> for authentication, authorization, and encryption.<br>
</li><li><b>
Web3 / blockchain experience</b> &mdash; especially smart contracts and dApps.<br>
</li></ul><h2 class="jobSectionHeader"><b>
Why You&rsquo;ll Love Working Here:</b></h2><ul><li><b>
Competitive cash + equity package</b> &mdash; we believe in rewarding impact.<br>
</li><li><b>
Healthcare coverage</b> for you and your family.<br>
</li><li><b>
Fast-growth startup environment</b> &mdash; with real ownership from day one.<br>
</li><li><b>
Collaborative, high-impact culture</b> &mdash; where innovation and execution thrive.<br>
</li><li><b>
Shape the future</b> &mdash; play a key role in how AI and machines transact globally.<p><b>
Interview Process</b></p><ul><li>
Recruiter Screen</li><li>
Woven Assessment</li><li>
Hiring Manager Screen</li><li>
Live Coding</li><li>
System Design</li><li>
Commitment Interview</li></ul></li></ul><p>
</p><p>kvxPcdFoav</p></div>

Senior Backend Engineer

Anthropic

<div>
 <strong>
  About Anthropic
  <br/>
  <br/>
 </strong>
 Anthropic&rsquo;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.
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 We're seeking an exceptional Research Scientist to join our pioneering Life Sciences team at Anthropic. Our team is building a world-class research group focused on making fundamental biological discoveries. This role combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery.
 <br/>
 <br/>
 As a founding member of Life Sciences, you'll work in a high-impact team that operates at the intersection of computational and experimental biology. You'll help establish Anthropic as a leader in biology research while developing product intuition through direct engagement with the challenges and opportunities of laboratory science.
 <br/>
 <br/>
 <strong>
  Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Implement a scientific research program that may include both wet-lab and computational components
  </li>
  <li>
   Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications
  </li>
  <li>
   Develop and execute strategies to systematically improve model performance on scientific tasks
  </li>
  <li>
   Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery
  </li>
  <li>
   Translate between biological domain knowledge and machine learning objectives
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  You May Be a Good Fit If You
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Have at least 8 years of hands-on experience in life sciences R&amp;D, with deep expertise in areas such as molecular biology, drug discovery, or computational biology
  </li>
  <li>
   Have a track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
  </li>
  <li>
   Basic proficiency in Python and familiar with ML development practices
  </li>
  <li>
   Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
  </li>
  <li>
   Can work independently while maintaining strong collaboration with cross-functional teams
  </li>
  <li>
   Are results-oriented, with a bias towards flexibility and impact
  </li>
  <li>
   Thrive in a fast-paced research environment where you balance rigorous scientific standards with rapid iteration
  </li>
  <li>
   Are passionate about using AI to accelerate scientific discovery while maintaining high ethical standards
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Strong Candidates May Also Have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Ph.D. in a biological science (molecular biology, biochemistry, bioengineering, computational biology), or in a related field, or equivalent industry experience
  </li>
  <li>
   Published research or practical experience in scientific AI applications
  </li>
  <li>
   Familiarity with modern machine learning techniques and model training methodologies
  </li>
  <li>
   Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools
   <br/>
   <br/>
  </li>
 </ul>
 This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for human health and scientific understanding. If you're excited about using your expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you.
 <br/>
 <br/>
 <strong>
  Role-specific policy:
 </strong>
 For this role, we expect staff to be able to work from our San Francisco office at least 3 days a week, though we encourage you to apply even if you might need some flexibility for an interim period of time.
 <br/>
 <br/>
 The expected base compensation for this position is below. Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.
 <br/>
 <br/>
 <strong>
  Annual Salary
  <br/>
  <br/>
 </strong>
 $300,000&mdash;$320,000 USD
 <br/>
 <br/>
 <strong>
  Logistics
  <br/>
  <br/>
 </strong>
 <strong>
  Education requirements:
 </strong>
 We require at least a Bachelor's degree in a related field or equivalent experience.
 <br/>
 <br/>
 <strong>
  Location-based hybrid policy:
 </strong>
 Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
 <br/>
 <br/>
 <strong>
  Visa sponsorship:
 </strong>
 We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
 <br/>
 <br/>
 <strong>
  We encourage you to apply even if you do not believe you meet every single qualification.
 </strong>
 Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
 <br/>
 <br/>
 <strong>
  How We're Different
  <br/>
  <br/>
 </strong>
 We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact &mdash; advancing our long-term goals of steerable, trustworthy AI &mdash; rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
 <br/>
 <br/>
 The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
 <br/>
 <br/>
 <strong>
  Come work with us!
  <br/>
  <br/>
 </strong>
 Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
 <strong>
  Guidance on Candidates' AI Usage:
 </strong>
 Learn about our policy for using AI in our application process
</div>


Research Scientist, Life Sciences

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