data analyst

data scientist

financial analyst

research assistant

academic human resources analyst 3 Jobs in San Francisco

academic human resources analyst 3

ai researcher

RamAIn (YC W26)

<div>
 RamAIn builds the
 
 world's fastest computer-use agents for enterprise work.
 
 We're a YC W26 company on a mission to eliminate repetitive, manual workflows by training AI agents that operate legacy systems, desktop apps, and web portals the same way humans do - but 10&times; faster and more reliably.
 
 
 Founders
 
 
 RamAIn was founded by Shourya Vir Jain (CEO) and Vansh Ramani (CTO), who met at IIT Delhi and dropped out to build AI-native automation for enterprise workflows.
 
 
 Shourya previously worked at McKinsey, where he saw firsthand how much enterprise work still depends on manual interaction with legacy systems. He is also a FIDE-rated chess player (2118), was ranked top-20 globally under 16, having previously built and scaled an enterprise AI company to over 6-figures in ARR.
 
 
 Vansh is an AI researcher who worked at CMU on scalable machine learning and representation learning. His research includes publications at ICLR and ACS, and &ldquo;Panaroma,&rdquo; one of the fastest vector search algorithms, later merged into Meta&rsquo;s FAISS. He focuses on building high-performance reasoning and planning systems for real-world deployment.
 
 
 Together, they started RamAIn to build the fastest computer-use agents for enterprise work - combining deep research with production-grade systems that operate reliably across messy, real-world software environments.
 
 
 Role
 
 
 We're hiring an early-career Founding AI/ML researcher to join our core team and help build agents that reason, plan, and execute complex workflows autonomously. This role sits at the intersection of cutting-edge research and production systems - you won&rsquo;t just prototype ideas, you&rsquo;ll ship them to real enterprise customers.
 
 
 You might be a strong fit you have a Bachelor's degree with exceptional hands-on experience building agentic systems or have recently completed a PhD or Master's in machine learning, AI, robotics or RL pipelines, multimodal models, or a related field.
 
 
 What you'll work on
 
 
 <ul>
 <li>
 Designing planning and reasoning architectures for long-horizon computer-use agents\
 </li>
 <li>
 Training multimodal models that understand UI layouts, screenshots, and DOM structures\
 </li>
 <li>
 Building fast action-selection systems that operate across messy, real-world software\
 </li>
 <li>
 Improving reliability, latency, and robustness of deployed agents\
 </li>
 <li>
 Running experiments and rapidly shipping improvements to production
 
 
 </li>
 </ul>
 Expectations
 
 
 This is not a research-only role. Within your first few weeks, you&rsquo;ll be shipping models, running experiments on real workflows, and seeing your work automate production tasks used by enterprise teams.
 
 
 We're looking for someone who
 
 
 <ul>
 <li>
 Moves quickly from idea &rarr; experiment &rarr; deployment\
 </li>
 <li>
 Has built real AI agent orchestration systems\
 </li>
 <li>
 Is excited about agentic AI, reasoning systems, experimentation or automation\
 </li>
 <li>
 Thrives in a fast-moving early-stage startup environment
 
 
 </li>
 </ul>
 You&rsquo;ll work directly with the founders to push the frontier of computer-use agents and help define a new category of AI-native enterprise automation.
</div>

Founding AI/ML Research Engineer

Loop

<div>
 
 About Loop
 
 
 
 Loop is on a mission to unlock profits trapped in the supply chain (https://loop.com/article/unlock-profit-trapped-in-your-supply-chain) and lower costs for consumers. Bad data and inefficient workflows create friction that limits working capital and raises costs for every supply chain stakeholder.
 
 
 Loop&rsquo;s modern audit and pay platform uses our domain-driven AI to harness the complexity of supply chain data and documentation. We improve transportation spend visibility so companies can control their costs and power profit. That is why industry leaders like J.P. Morgan Chase, Great Dane, Emerge, and Loadsmart work with Loop.
 
 
 Our investors include J.P. Morgan, Index Ventures, Founders Fund, 8VC, Susa Ventures, Flexport, and 50 industry-leading angel investors. Our team brings subject matter expertise from companies like Uber, Google, Flexport, Meta, Samsara, Intuit, Rakuten, and long-standing industry leaders like C.H. Robinson.
 
 
 
 About The Role
 
 
 
 Loop is growing its AI team and you&rsquo;ll have the opportunity to build both AI models and features that directly impact Loop&rsquo;s business. Loop is positioned to disrupt the incumbent freight audit and pay market, and is one of the companies leading the AI services wave, utilizing AI to automate complex back-office workflows and tasks. You will face and solve many complex technical challenges while you receive guidance and feedback from the team. The range of work here is broad, you can work on everything from training and deploying in-house multimodal LLMs, scaling our inference infrastructure, or building out and shipping agentic workflows. In doing so, you&rsquo;ll have the opportunity to define how the AI and broader Loop team will grow.
 
 
 
 What you will work on
 
 
 
 Our primary focus has been on document extraction and understanding, where we utilize multimodal LLMs, including our own in-house foundation model, to extract, normalize, and link data together into our domain model. As Loop&rsquo;s customers rely on Loop to ingest and normalize highly accurate data, we hold ourselves to a high standard to build models with a very high level of accuracy. In tandem, Loop&rsquo;s machine learning platform requires a high degree of reliability and scalability, and we expect our training and inference volume to scale several orders of magnitude in the coming year. Going forward, Loop will expand its AI capabilities, expanding into other areas such as workflow automation and audit, where we will utilize agents to tackle these problems.
 
 
 This role spans multiple domains:
 
 
 <ul>
 <li>
 ML modeling &ndash; training, evaluating, and deploying models.
 </li>
 <li>
 AI engineering &ndash; utilizing and orchestrating API LLM models to build agents to solve business problems at Loop.
 </li>
 <li>
 Backend engineering &ndash; building out frameworks to enable AI agents at scale.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 1+ years of hands-on experience in deep learning frameworks (e.g., PyTorch, Tensorflow, etc.) or shipping agents to production.
 </li>
 <li>
 Solid understanding of fundamental ML algorithms, especially multimodal LLMs.
 </li>
 <li>
 Experience fine-tuning LLMs and deploying them to production or building out agentic systems.
 </li>
 <li>
 Ability to ship high-quality code to production.
 </li>
 <li>
 Experience in cloud environments (AWS, Google, Azure).
 </li>
 <li>
 Strong communication skills and willingness to collaborate in a cross-functional team environment with domain experts.
 </li>
 <li>
 Keep up to date with the latest AI research.
 </li>
 <li>
 Ability to work in a fast-paced environment.
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 <ul>
 <li>
 Base pay 130k - 200k
 
 
 </li>
 </ul>
 
 Benefits &amp; Perks
 
 
 
 <ul>
 <li>
 Premium Medical, Dental, and Vision Insurance plans
 </li>
 <li>
 Insurance premiums covered 100% for you
 </li>
 <li>
 Unlimited PTO
 </li>
 <li>
 Fireside chats with industry leading keynote speakers
 </li>
 <li>
 Off-sites in locales such as Napa and Tahoe
 </li>
 <li>
 Generous professional development budget to feed your curiosity
 </li>
 <li>
 Physical and Mental fitness subsidies for yoga, meditation, gym, or ski memberships
 </li>
 </ul>
</div>

AI Engineer

Prolific

<div>
 
 AI Trainer &ndash; Research Scientists
 
 
 
 
 
 About Prolific
 
 
 
 
 Prolific is not just another player in the AI space &ndash; we are building the biggest pool of quality human data in the world.
 
 
 Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.
 
 
 
 
 The role
 
 
 
 
 We&rsquo;re looking for people with Research Scientist experience to work as self-employed AI Trainers to help train and evaluate cutting-edge AI models from some of the world&rsquo;s biggest AI labs. If you have the necessary experience, we&rsquo;ll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you&rsquo;ll be invited to join Prolific as a Domain Expert participant, where you&rsquo;ll get paid to train and evaluate powerful AI models.
 
 
 Researchers looking for your skills tend to pay up to $50/hr subject to the length of tasks available. If successful, you must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter.
 
 
 
 
 What you&rsquo;ll be doing in the role
 
 
 
 
 Completing AI training tasks such as analyzing, editing, and writing research-focused content
 
 
 Judging the performance of AI on research design, methodology, and scientific reasoning prompts
 
 
 Improving cutting-edge AI models using your experience in research and experimentation
 
 
 
 
 What you&rsquo;ll bring
 
 
 
 
 Experience working as a Research Scientist or in a closely related research role
 
 
 A strong understanding of scientific methodology, experimental design, and data analysis
 
 
 A willingness to take our skills verification test to assess your suitability for our Domain Expert participant pool
 
 
 Strong attention to detail and the ability to concentrate on complex tasks for up to one hour at a time
 
 
 A reliable and fast internet connection and access to a computer
 
 
 A Paypal account to receive payment from our clients*
 
 
 
 
 Why Prolific is a great platform to join as a Domain Expert participant
 
 
 
 
 Joining our platform as a Prolific participant will give you the chance to influence the AI models of the future. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home.
 
 
 We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.
 
 
 We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation &ndash; one that reflects the breadth and the best of humanity.
 
 
 <ul>
 <li>
 Participants are self-employed and need to declare earnings to their respective tax authorities. Prolific has no liability for any tax obligations or reporting requirements resulting from compensation received through this platform. Each participant is solely responsible for compliance with applicable tax laws in their jurisdiction.
 
 
 </li>
 </ul>
 
 Links to more information on Prolific
 
 
 
 Website
 
 
 Youtube
 
 
 Privacy Statement
 
 
 By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal information.
</div>

AI Training - Research Scientist (PST)

AGI, Inc.

<div>
 
 Think Different. Build the Future. 🚀
 
 
 
 Our Mission
 
 
 
 Build everyday AGI. Trustworthy, consumer-grade agents that redefine human&ndash;AI collaboration for millions.
 
 Software shouldn&rsquo;t wait for commands; it should partner with you, amplifying what you can do every single day.
 
 
 Why AGI, Inc.
 
 
 We&rsquo;re a stealth team of elite founders and AI researchers, with backgrounds spanning
 
 Stanford, OpenAI, and DeepMind
 
 . We&rsquo;re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale.
 
 
 Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts.
 
 
 We are supported by tier-1 investors who funded the first generation of AI giants; now they&rsquo;re backing us to build the next:
 
 everyday AGI. (Watch the
 
 
 demo
 
 
 )
 
 
 
 If you see possibility where others see limits, read on.
 
 
 
 About The Role
 
 
 
 As an Applied Scientist focused on Evaluation &amp; Model Behavior, you will design and implement the systems used to measure and improve the performance of Computer Use Agents.
 
 
 This is not a support role. You will be responsible for the technical definition of model quality, including the design of evaluation metrics, the curation of training datasets, and the engineering of system prompts. You'll work directly with the engineering team to translate product requirements into technical specifications and quantifiable benchmarks.
 
 
 You'll focus on rigor, clarity, and impact, ensuring every metric, dataset, and prompt moves us toward more reliable, trustworthy agents.
 
 
 
 What You'll Do
 
 
 
 
 Model Behavior Design:
 
 Translate product requirements into technical specifications for model behavior. Engineer system prompts and few-shot examples to address specific capability gaps and behavioral failures.
 
 
 
 Evaluation Design:
 
 Define metrics for reasoning, tool usage, and safety, and validate these metrics against human judgment to ensure statistical rigor.
 
 
 
 Data Strategy:
 
 Design algorithms to filter, score, and select training data. Write Python scripts to sanitize inputs and manage the training data lifecycle from raw logs to high-quality datasets.
 
 
 
 Failure Analysis:
 
 Investigate regressions in model benchmarks. Diagnose root causes, distinguishing between data quality issues, prompt instruction failures, or underlying model capability gaps and implement fixes.
 
 
 
 Ground Truth Management:
 
 Define rubrics and guidelines for human annotation. Maintain reference datasets ("Golden Sets") to establish a consistent baseline for model performance evaluation.
 
 
 
 Minimum Qualifications
 
 
 
 <ul>
 <li>
 Master's degree or PhD in Computer Science, Data Science, Statistics, or a related technical field, or equivalent practical experience
 </li>
 <li>
 3+ years of experience in Data Science, Machine Learning, or Applied Science
 </li>
 <li>
 Proficiency in Python, with experience writing production-quality code for data pipelines or evaluation harnesses
 </li>
 <li>
 Experience with experimental design, A/B testing, or statistical analysis
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with Large Language Models (LLMs), including prompt engineering, fine-tuning, or RLHF workflows
 </li>
 <li>
 Experience building automated evaluation systems or implementing model-based evaluation frameworks
 </li>
 <li>
 Ability to translate product requirements into measurable technical metrics
 </li>
 <li>
 Experience managing human-in-the-loop data pipelines or annotation quality control
 
 
 </li>
 </ul>
 
 Why This Role Matters
 
 
 
 You can't improve what you can't measure. You can't ship what you can't trust.
 
 
 You will define the technical definition of quality for our agents &mdash; the metrics that predict real-world success, the datasets that encode user intent, and the prompts that shape model behavior. Your work will directly determine how quickly we can iterate and how confidently we can ship.
 
 
 
 Our Culture
 
 
 
 🏢 All in, in person &mdash; work moves faster face-to-face
 
 
 🚀 Ship by default &mdash; speed and polish can coexist
 
 
 🤝 One band, one sound &mdash; radical candor, zero politics
 
 
 
 Perks
 
 
 
 🏥 Competitive company-sponsored medical, dental, and vision insurance
 
 
 ✈️ Top-tier relocation and immigration support
 
 
 
 How To Apply
 
 
 
 Send us:
 
 
 <ul>
 <li>
 A link &mdash; or 60-second video &mdash; of something you built and why it matters
 </li>
 <li>
 Your resume or LinkedIn
 </li>
 <li>
 Two sentences on the hardest problem you've cracked
 
 
 </li>
 </ul>
 Every exceptional candidate hears back within 48 hours.
 
 
 If you see possibility where others see limits, we'd love to meet you.
 
 
</div>

Applied Data Scientist, Evaluation & Model Behavior

<div><h1 class="jobSectionHeader">Think Different. Build the Future.
</h1><h2 class="jobSectionHeader">Our Mission</h2>
Build everyday AGI. Trustworthy, consumer-grade agents that redefine human&ndash;AI collaboration for millions. Software shouldn&rsquo;t wait for commands; it should partner with you, amplifying what you can do every single day.<h2 class="jobSectionHeader">
Why AGI, Inc.</h2>
We&rsquo;re a stealth team of elite founders and AI researchers, with backgrounds spanning Stanford, OpenAI, and DeepMind. We&rsquo;re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale.
Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts.
We are supported by tier-1 investors who funded the first generation of AI giants; now they&rsquo;re backing us to build the next: everyday AGI. (Watch the demo)
If you see possibility where others see limits, read on.<h2 class="jobSectionHeader">
About the Role</h2>
As an Applied Scientist focused on Evaluation &amp; Model Behavior, you will design and implement the systems used to measure and improve the performance of Computer Use Agents.
This is not a support role. You will be responsible for the technical definition of model quality, including the design of evaluation metrics, the curation of training datasets, and the engineering of system prompts. You'll work directly with the engineering team to translate product requirements into technical specifications and quantifiable benchmarks.
You'll focus on rigor, clarity, and impact, ensuring every metric, dataset, and prompt moves us toward more reliable, trustworthy agents.<h2 class="jobSectionHeader">
What You'll Do</h2>
Model Behavior Design: Translate product requirements into technical specifications for model behavior. Engineer system prompts and few-shot examples to address specific capability gaps and behavioral failures.
Evaluation Design: Define metrics for reasoning, tool usage, and safety, and validate these metrics against human judgment to ensure statistical rigor.
Data Strategy: Design algorithms to filter, score, and select training data. Write Python scripts to sanitize inputs and manage the training data lifecycle from raw logs to high-quality datasets.
Failure Analysis: Investigate regressions in model benchmarks. Diagnose root causes, distinguishing between data quality issues, prompt instruction failures, or underlying model capability gaps and implement fixes.
Ground Truth Management: Define rubrics and guidelines for human annotation. Maintain reference datasets (&quot;Golden Sets&quot;) to establish a consistent baseline for model performance evaluation.<h2 class="jobSectionHeader">
Minimum Qualifications</h2><ul><li>
Master's degree or PhD in Computer Science, Data Science, Statistics, or a related technical field, or equivalent practical experience</li><li>
3+ years of experience in Data Science, Machine Learning, or Applied Science</li><li>
Proficiency in Python, with experience writing production-quality code for data pipelines or evaluation harnesses</li><li>
Experience with experimental design, A/B testing, or statistical analysis</li></ul><h2 class="jobSectionHeader">
Preferred Qualifications</h2><ul><li>
Experience with Large Language Models (LLMs), including prompt engineering, fine-tuning, or RLHF workflows</li><li>
Experience building automated evaluation systems or implementing model-based evaluation frameworks</li><li>
Ability to translate product requirements into measurable technical metrics</li><li>
Experience managing human-in-the-loop data pipelines or annotation quality control</li></ul><h2 class="jobSectionHeader">
Why This Role Matters</h2>
You can't improve what you can't measure. You can't ship what you can't trust.
You will define the technical definition of quality for our agents &mdash; the metrics that predict real-world success, the datasets that encode user intent, and the prompts that shape model behavior. Your work will directly determine how quickly we can iterate and how confidently we can ship.<h2 class="jobSectionHeader">
Our Culture</h2>
All in, in person &mdash; work moves faster face-to-face
 
Ship by default &mdash; speed and polish can coexist
 
One band, one sound &mdash; radical candor, zero politics<h2 class="jobSectionHeader">
Perks</h2>
Competitive company-sponsored medical, dental, and vision insurance
 
Top-tier relocation and immigration support<h2 class="jobSectionHeader">
How to Apply</h2>
Send us:<ul><li>
A link &mdash; or 60-second video &mdash; of something you built and why it matters</li><li>
Your resume or LinkedIn</li><li>
Two sentences on the hardest problem you've cracked</li></ul>
Every exceptional candidate hears back within 48 hours.
If you see possibility where others see limits, we'd love to meet you.</div>

<div>
 
 Think Different. Build the Future. 🚀
 
 
 
 Our Mission
 
 
 
 Build everyday AGI. Trustworthy, consumer-grade agents that redefine human&ndash;AI collaboration for millions.
 
 Software shouldn&rsquo;t wait for commands; it should partner with you, amplifying what you can do every single day.
 
 
 Why AGI, Inc.
 
 
 We&rsquo;re a stealth team of elite founders and AI researchers, with backgrounds spanning
 
 Stanford, OpenAI, and DeepMind
 
 . We&rsquo;re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale.
 
 
 Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts.
 
 
 We are supported by tier-1 investors who funded the first generation of AI giants; now they&rsquo;re backing us to build the next:
 
 everyday AGI. (Watch the
 
 
 demo
 
 
 )
 
 
 
 If you see possibility where others see limits, read on.
 
 
 
 What You&rsquo;ll Do
 
 
 
 
 Training Automation:
 
 Design and implement robust CI/CD pipelines for machine learning workflows. Automate nightly and on-demand training runs, including data ingestion, job orchestration, checkpointing, and artifact management, with reliability as a first-class requirement.
 
 
 
 Evaluation Infrastructure:
 
 Build scalable evaluation harnesses that automatically benchmark models on every merge. Optimize latency and resource usage so experimentation stays fast, and performance regressions are caught immediately.
 
 
 
 Research Tooling:
 
 Develop internal SDKs, CLIs, and lightweight UIs (e.g., Streamlit, Retool) that empower researchers to:
 
 
 <ul>
 <li>
 Inspect trajectories and traces
 </li>
 <li>
 Visualize model failures
 </li>
 <li>
 Curate and manage datasets
 </li>
 <li>
 Iterate without friction
 
 
 </li>
 </ul>
 You&rsquo;ll make experimentation ergonomic.
 
 
 
 Observability &amp; Performance:
 
 Implement comprehensive tracking for:
 
 
 <ul>
 <li>
 Model latency, throughput, and error rates
 </li>
 <li>
 GPU utilization and cluster health
 </li>
 <li>
 Inference cost and unit economics
 
 
 </li>
 </ul>
 Build dashboards and alerting systems that give real-time visibility into system performance and reliability.
 
 
 
 Minimum Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Computer Science, Engineering, or equivalent practical experience
 </li>
 <li>
 3+ years in Software Engineering, MLOps, or ML Infrastructure
 </li>
 <li>
 Strong Python proficiency
 </li>
 <li>
 Experience building internal developer tools, CLIs, or dashboards
 </li>
 <li>
 Experience with cloud infrastructure (AWS or GCP) and containerization (Docker, Kubernetes)
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience designing CI/CD pipelines specifically for ML workflows
 </li>
 <li>
 Familiarity with LLM serving stacks such as vLLM or TGI
 </li>
 <li>
 Experience managing GPU clusters and optimizing distributed workloads
 
 
 </li>
 </ul>
 
 Why This Role Matters
 
 
 
 Great research without great infrastructure slows to a crawl.
 
 
 Great infrastructure multiplies the impact of every researcher.
 
 
 You will define how experiments scale, how reliability is measured, and how quickly we can ship improvements to real users. The systems you build will directly shape the speed and quality of our progress toward everyday AGI.
 
 
 
 Our Culture
 
 
 
 🏢 All in, in person &mdash; work moves faster face-to-face
 
 
 🚀 Ship by default &mdash; novel and polished can coexist, speed is the feature
 
 
 🤝 One band, one sound &mdash; radical candor, zero politics, help each other win
 
 
 
 Perks
 
 
 
 🏥 Competitive company-sponsored medical, dental, and vision insurance
 
 
 ✈️ Top-tier relocation and immigration support
 
 
 
 How To Apply
 
 
 
 Send us:
 
 
 <ul>
 <li>
 A link &mdash; or 60-second video &mdash; of something you built and why it matters
 </li>
 <li>
 Your resume or LinkedIn
 </li>
 <li>
 Two sentences on the hardest problem you've cracked
 
 
 </li>
 </ul>
 Every exceptional candidate hears back within 48 hours.
 
 
 If you see possibility where others see limits, we'd love to meet you.
 
 
</div>

ML Platform & Infrastructure Engineer

Stealth Startup

<div>
 
 The Culture We Are Building
 
 <ul>
 <li>
 We are assembling a lean group of generalists. While we welcome experience, we're primarily looking for creative, curious problem solvers with a keen interest in open-ended, technically challenging problems.
 </li>
 <li>
 We are looking to hire in-person in San Francisco. We believe that having a community is critical for learning, growth, and camaraderie. We will pay generously for relocation assistance.
 </li>
 <li>
 We want to invest heavily in our team's growth. We will happily sponsor conference attendance and encourage projects and research. If there's something that will help you grow as an engineer, researcher, or designer, we actively want to support it.
 </li>
 <li>
 We believe in compensating exceptional talent exceptionally well. We are an early stage company, but are committed to working with candidates we're excited about to ensure they are excited too.
 </li>
 <li>
 We have an academic as well as competitive spirit. We enjoy thought exercises, math puzzles, intellectual inquiry, and vigorous debates.
 </li>
 </ul>
 
 
 
 
 Role: Machine Learning Engineer
 
 
 Design, train, and ship models that guide reviewers to the most important changes, resolve merge conflicts more safely, and make diffs understandable through semantic grouping and summarization. You'll combine retrieval, code‑aware modeling, and small‑model techniques to deliver low‑latency, cost‑effective ML in production.
 
 
 Responsibilities
 
 <ul>
 <li>
 -Train and fine‑tune models that predict the next location a reviewer should focus on: ranking files, hunks, and comments
 </li>
 <li>
 -Build ML‑assisted merge conflict resolution: align and reconcile hunks using code context/AST, propose safe resolutions, and explain changes
 </li>
 <li>
 -Develop semantic change grouping and diff visualization: detect refactors/moves, cluster related edits across files, and generate concise summaries
 </li>
 <li>
 -Evaluate retrieval and RAG for pull requests: retrieve relevant context, discussions, and history to improve grouping, prioritization, and summarization
 </li>
 <li>
 -Design offline/online evaluation: labeled datasets, ranking metrics (e.g., MAP/NDCG), A/B experiments, and human‑in‑the‑loop feedback
 </li>
 <li>
 -Operate efficient inference: apply small‑model techniques (LoRA, supervised fine‑tuning, distillation, quantization)
 </li>
 </ul>
 
 Qualifications
 
 <ul>
 <li>
 -Strong background in deep learning research and implementation, with experience in modern architectures and concepts (transformers, MoE, reasoning, etc.)
 </li>
 <li>
 -Track record publishing results at top-tier conferences (NeurIPS, ICML, ICLR)
 </li>
 <li>
 -Expertise in Python and ML frameworks (PyTorch, JAX)
 </li>
 <li>
 -Experience with MLOps and model optimization (ONNX, TensorRT, serving infrastructure)
 </li>
 <li>
 -Familiarity with cost‑effective techniques: LoRA, supervised fine‑tuning, distillation, and quantization for small/efficient models
 </li>
 <li>
 -Familiarity with systems programming across the stack: Rust/C/C++, CUDA/GPU optimization, and kernel-level performance tuning
 </li>
 <li>
 -Experience with reinforcement learning, particularly in systems optimization and decision-making tasks
 </li>
 </ul>
 
 
</div>

Machine Learning Engineer [32985]

Explore 222+ Ai researcher in San Francisco this April. Find roles across top companies and apply now to boost your career.

222 ai researcher jobs In San Francisco

Founding AI/ML Research Engineer

AI Engineer

AI Training - Research Scientist (PST)

Applied Data Scientist, Evaluation & Model Behavior

Applied Data Scientist, Evaluation & Model Behavior

ML Platform & Infrastructure Engineer

Machine Learning Engineer [32985]