Founding Machine Learning EngineerLocation: San Francisco, CA Work Model: In-office 5 days a weekAbout UsAt Effective AI, we are pioneering the future of work. Our vision is to push the boundaries of AI beyond mere repetitive tasks, focusing instead on intricate knowledge work that requires expertise and multi-faceted reasoning. We are developing advanced AI Teammates that are designed to navigate complex workflows and collaborate seamlessly with human professionals. Our initial focus is on the trillion-dollar U.S. Property & Casualty insurance sector, a domain rich with complexity and data, making it an ideal arena for our innovations.We proudly secured $10 million in seed funding from prominent investors including Lightspeed Ventures and Valor Equity Partners.Our committed team is based in San Francisco and thrives on in-person collaboration to tackle these significant challenges.Your RoleAs a Founding Machine Learning Engineer, you will be an integral member of our founding team, responsible for architecting, training, and deploying the agent loops that power our AI Teammates from inception. You will address some of the most pressing challenges in agentic AI and natural language processing, developing AI solutions adept at performing essential insurance functions such as underwriting and claims processing.Your responsibilities will include:Architecting and Developing Core ML Pipelines: Design, train, and fine-tune cutting-edge language models (including reinforcement learning agents) to facilitate long-term task accomplishment and complex decision-making.Implementing Nuanced Reasoning: Integrate machine learning techniques that empower agents to make informed decisions based on ambiguous or incomplete data, akin to human expert reasoning and generalization.Building Intelligent, Tool-Using Agents: Engineer the ML systems that enable our agents to dynamically select and utilize a broad array of external tools—including APIs, databases, web searches, and Excel-based pricing algorithms—to gather necessary information and execute actions.Designing and Implementing Robust Evaluation Frameworks: Create and employ comprehensive evaluation metrics and systems to rigorously assess and benchmark agent performance, identify areas for enhancement, and guarantee reliability and safety in real-world insurance processes.Enabling Continuous Adaptation and Learning: Develop resilient ML pipelines and feedback loops that facilitate ongoing learning and adaptation.

About TroveTrove is pioneering an advanced AI assistant tailored for financial institutions, encompassing enterprise search and intelligent agents for private equity firms, hedge funds, and banks.Our mission is to bring associate-level artificial general intelligence (AGI) to the forefront.We have successfully raised nearly $10 million in funding from renowned investors, including Menlo Ventures and Khosla Ventures.Our initial traction is impressive, featuring significant enterprise contracts and user engagement levels that rival top-tier B2B AI applications.Why Join Us?Impactful Role: From your first day, you'll play a critical role in shaping our product and culture, tackling unexplored challenges in generative AI applications, including enterprise-scale data retrieval, autonomous agents, and the development of knowledge graphs.Competitive Compensation: Enjoy a compensation package that includes over 0.5% equity and a salary range between $175,000 and $300,000, allowing you to hold a significant stake in the company.Talented Team: Join a remarkable founding team in San Francisco, featuring leaders with proven track records, such as Shivaal Roy (CTO), a former founding engineer at Glean with over $100 million ARR, and Danny Goldman (CEO), a high achiever in Bain's Private Equity group.Your ResponsibilitiesAdvance the frontiers of generative AI by implementing human-level capabilities.Create and implement innovative algorithms for knowledge graphs to enhance search and retrieval processes across both structured and unstructured firm data.Collaborate with leading AI laboratories and top-tier financial institutions to develop reliable, AI-driven systems.About YouDemonstrated history of outstanding performance in software engineering.3 to 8 years of relevant experience in software engineering.Eager to work in-person four days a week in San Francisco.Preferred but not mandatory: familiarity with large language models, search and retrieval systems, and previous experience in the financial sector.

Join Orchard as a Machine Learning Engineer and play a pivotal role in transforming data into actionable insights. In this dynamic position, you will leverage your expertise in machine learning algorithms and data analysis to develop innovative solutions that enhance our products and services.We are looking for a proactive team player who thrives in a fast-paced environment and possesses strong problem-solving skills. You will collaborate with cross-functional teams, engage with large datasets, and contribute to the design and implementation of machine learning models.

Join Us as a Founding Data Scientist and Machine Learning EngineerAmplify Your ImpactYou have achieved remarkable milestones in your career—delivering impactful models, influencing key metrics, and showcasing the transformative potential of data science and machine learning. You have positively affected products that touch millions of lives.Now, envision the possibility of enhancing the entire app ecosystem by extending your influence across numerous products and companies, making every app in users’ pockets smarter, more engaging, and indispensable.Your expertise can empower product teams to innovate faster, captivate users, and drive revenue growth, thanks to the intelligence you develop once and deploy universally.We share this ambition; we have successfully achieved it multiple times at leading organizations like Uber, Apple, Meta, Google, and Chime. Our contributions have generated tens of billions of dollars in impacts for essential products relied on by billions, and we are poised to elevate our influence further.If this resonates with the journey you seek, we invite you to continue reading.Our MissionDashboards recount the past; teams require insights for their next move. Palladio AI serves as the intelligence layer between raw data and decisive action, illuminating product opportunities that translate into genuine growth levers and guiding actions so product teams can iterate with confidence and speed rather than wade through noise.Your RoleYou will be part of a team crafting foundational systems in behavioral modeling, causal inference, forecasting, agentic platforms, and beyond. Your contributions will extend these domains: developing ML and AI models to identify and highlight product opportunities, deploying learning loops that enhance with each release. In essence, you will convert fundamental data science principles into a scalable product across various industries.Beyond technical challenges, you will create a platform that aids real people in making informed decisions, transforming data into clarity and clarity into actionable progress.Your ProfilePassion for Craft and Excellence. You dive into complex datasets, prototype swiftly, and refine until insights shine.Impact-Driven Mindset. 6+ years of experience in production ML/DS; you harmonize scientific rigor with a practical approach—“it ships today, iteration follows.”

Join Our Innovative Team at HiveHive is at the forefront of cloud-based AI solutions, revolutionizing how organizations understand, search for, and generate content. Trusted by many of the world's largest and most groundbreaking companies, we empower developers with premier pre-trained AI models that handle billions of API requests monthly. Our turnkey software applications leverage proprietary AI models and datasets, driving transformative advancements in content moderation, brand protection, sponsorship measurement, and context-based ad targeting.With over $120M in funding from prominent investors like General Catalyst, 8VC, Glynn Capital, Bain & Company, and Visa Ventures, Hive is rapidly expanding. Our dynamic team of over 250 employees operates from our San Francisco, Seattle, and Delhi offices. If you are passionate about shaping the future of AI, we invite you to explore opportunities with us!About the Machine Learning Engineer RoleAs we strive to achieve our ambitious vision, we seek exceptional machine learning engineers to join our team. We are looking for enthusiastic developers who are eager to remain at the cutting edge of deep learning technology, designing and deploying state-of-the-art neural network models into production. Our ideal candidates thrive in working with large-scale datasets and demonstrate a keen interest in mastering new technologies across the machine learning spectrum. We value individuals who are proactive and take ownership of their projects, contributing innovative ideas and practical implementations. Experience in building machine learning applications from the ground up and designing scalable, maintainable data pipelines is essential.

Join Handshake as a Machine Learning Engineer I, where you will have the opportunity to work on cutting-edge machine learning projects that drive our innovative solutions. Collaborate with a talented team to develop algorithms and models that enhance our product offerings and improve user experiences.

Join our dynamic Personalization team at Boomtrain as a Machine Learning Engineer. We are in search of a skilled engineer who will play a pivotal role in developing and enhancing our recommendation systems that cater to a variety of customers.In this role, you will collaborate with a talented team dedicated to designing and implementing innovative models and systems that deliver personalized recommendations. You will have the opportunity to work on complex engineering challenges and contribute to generating hundreds of millions of recommendations daily.This position offers a unique chance to engage in end-to-end project work and make a significant impact on our personalization initiatives.Key Responsibilities:Research and propose advanced recommendation and optimization models to enhance our personalization systems.Develop and maintain offline model generation pipelines.Design and maintain online recommendation serving systems.

OverviewPulse is revolutionizing data infrastructure by addressing the critical challenge of extracting accurate, structured information from complex documents on a large scale. Our innovative approach to document understanding integrates intelligent schema mapping with advanced extraction models, outperforming traditional OCR and parsing methods.As a dynamic and rapidly growing team of engineers based in San Francisco, we empower Fortune 100 companies, Y Combinator startups, public investment firms, and growth-oriented businesses. With the backing of top-tier investors, we are on an exciting growth trajectory.What sets our technology apart is our cutting-edge multi-stage architecture:Layout comprehension with specialized component detection modelsLow-latency OCR models designed for targeted data extractionAdvanced algorithms for determining reading order in complex formatsProprietary table structure recognition and parsing capabilitiesFine-tuned vision-language models for interpreting charts, tables, and figuresIf you are passionate about the convergence of computer vision, natural language processing, and data infrastructure, your contributions at Pulse will directly influence our customers and shape the future of document intelligence.

Join Handshake as an Associate Machine Learning Engineer and embark on an exciting journey in the world of artificial intelligence and machine learning. In this role, you will collaborate with a talented team to develop innovative solutions that leverage cutting-edge technologies. You'll have the opportunity to contribute to real-world projects, enhancing your skills while driving impactful results.

About AbacusAt Abacus, we are revolutionizing the operations of accounting firms through innovative AI agents that automate the monotonous and repetitive tasks currently faced by teams. Our cutting-edge core engine integrates advanced Optical Character Recognition (OCR) and Large Language Models (LLMs) to provide genuinely intelligent back-office automation solutions. We have already established partnerships with significant enterprise clients, generating substantial revenue, and are proudly supported by leading investors. Having achieved this milestone as a compact team of two, we are eager to expand our workforce. If you are passionate about shaping the future of AI-driven operations and are ready to take on impactful technical challenges from the outset, this is your opportunity.The RoleWe are seeking our inaugural Founding Engineer to take charge of and enhance the core machine learning engine that powers Abacus. Reporting directly to the co-founders, you will work on-site from our San Francisco office. This is a unique chance to join a rapidly growing company as an early engineer, endowed with significant autonomy and influence over both product and technical direction.In your first 3–6 months, you will:Enhance and scale our ML extraction engine utilizing LLMs and OCR technologies.Take ownership of backend architecture and infrastructure decisions.Develop and launch innovative features that broaden the scope of automated workflows.In the following year, you will play a pivotal role in establishing the foundation of our engineering organization as we scale, while remaining hands-on and focused on delivering impactful results.What You’ll DoLead the development of our machine learning-driven document extraction engine.Architect backend systems with an emphasis on performance and scalability.Create and deploy new automation features tailored for tax firms.Work closely with the founders to align on product and user requirements.Contribute to defining our engineering culture and establishing the technical roadmap.What We're Looking ForEssential Qualifications:1–5 years of experience, preferably in environments with high ownership or early-stage ventures.Proficient backend development skills, especially in Python.Demonstrated ability as a 0→1 builder, whether through side projects, startup experience, or both.Commitment to work from our SF office, five days a week.Willingness to complete a technical assessment as part of the application process.

About PoesisPoesis is a pioneering AI-native investment manager that is transforming the landscape of U.S. equities through innovative foundation models. We are developing cutting-edge AI systems designed to predict market trends and exceed the performance of traditional investment managers. This exciting work represents frontier research that is validated in real-world scenarios. Your contributions will play a crucial role in influencing investment strategies and enhancing portfolio outcomes.Location & WorkstyleLocated in the vibrant San Francisco Bay Area, near Stanford, we support a Hybrid work model, requiring several days on-site each week.Relocation assistance is available.About the RoleAs a Founding Machine Learning Engineer, you will be the first full-time ML hire at Poesis, responsible for translating research and data into scalable production models. You will develop the initial ML pipelines from the ground up, managing everything from data ingestion and preprocessing to model training, validation, and signal generation. This role is ideal for a hands-on professional who excels in coding, designing experiments, and quickly delivering validated results.You will collaborate closely with the CEO and Chief Scientist, taking ownership of both the implementation process and iterative improvements. As the system scales, you will help transition it into a full production platform and establish best practices for future team members.ResponsibilitiesDesign, develop, and maintain the foundational ML infrastructure for Poesis’ investment platform.Create reproducible pipelines for data ingestion, feature engineering, and model training.Establish backtesting and evaluation frameworks with defined performance metrics.Provide regular, detailed reports on model accuracy, feature significance, and overall portfolio impact.Work closely with the Chief Scientist to refine model hypotheses and assess production readiness.Ensure high code quality through version control, testing, reproducibility, and thorough documentation.Develop robust backtesting frameworks and model validation tools, incorporating walk-forward evaluation and risk management controls.Integrate with leading financial data providers such as Bloomberg, FactSet, Refinitiv, and CapIQ.Implement foundational MLOps practices, including model versioning, CI/CD, monitoring, and documentation.Define and refine “demo-able” workflows that link model outputs to investment decision-makers.

Charter:Join us as a pivotal member of a groundbreaking team dedicated to revolutionizing the field of toxicology by developing advanced AI systems that will replace traditional lab and animal experiments.What We Seek:We are on the lookout for exceptional individuals who can inspire those around them and drive the team towards greatness. Our ideal candidate is someone with high agency—able to identify priorities and take action. We value unique passions and hobbies that may seem niche but reveal a deep commitment and curiosity when explored. Candidates should approach challenges with both intentionality and a sense of wonder, embodying the spirit of exploration akin to an immigrant in a new land or a self-taught coder. A strong desire to learn and grow, coupled with technical excellence and a commitment to mastering one’s craft, is essential. We want those who are willing to tackle daunting challenges and derive satisfaction from the journey as much as the outcome.Your Responsibilities:Establish the foundational end-to-end ML/AI system, including wetlab data generation, data cleaning/processing, model architecture, training, inference, and deployment strategies.Lead innovative research and development initiatives focused on elucidating the interplay between chemistry and biology.Design and scale large models that are pretrained on paired chemistry and biological imagery.Conduct applied research aimed at optimizing, aggregating, and pooling embeddings.Become a thought leader in emerging and underexplored domains, such as molecular graph representations and generative diffusion for biological applications.Develop entrepreneurial skills alongside engineering expertise by creating impactful solutions that deliver substantial value for scientists.Deliver outstanding technology and products that redefine industry standards.Preferred Attributes:...

About Pax HistoriaPax Historia is pioneering a new genre of gameplay by leveraging cutting-edge generative AI technologies. Our innovative platform combines the strategic depth of grand strategy games with the limitless creativity of a sandbox environment, driven by a vibrant community that actively creates and modifies scenarios.Our user base generates hundreds of scenarios daily and engages in millions of game rounds each week, exhibiting rapid growth. We are proud to be supported by esteemed investors such as Y Combinator, Pace Capital, and Z Fellows. Your contributions will have an immediate impact on a product enjoyed by hundreds of thousands of players.Position OverviewWe are seeking a founding-level ML Systems Engineer to join our team in-person full-time in San Francisco (Dogpatch). You will have the opportunity to work closely with our cofounders to shape the future of our technology.Current Challenges:Closed-source models yield satisfactory game performance but come with high costs.Open-source models are more budget-friendly yet often underperform in our environment.Prompts and harnesses exhibit minimal variance across models.We have a functional internal evaluation system with significant opportunities for enhancement.Your Responsibilities:Establish and manage the necessary infrastructure for customizing harnesses and prompts tailored to individual AI models to optimize their performance.Develop domain-specific models aimed at narrowing or completely bridging the gap in performance between open and closed models.Optimize caching strategies to decrease expenses associated with closed-source models.Enhance the performance of closed-source models by training specialized endpoints.Assess and advance the effectiveness of embedding and reranking mechanisms in our applications.Facilitate the creation of new user experiences based on emerging world models.In Summary: Your work will directly enhance the affordability and enjoyment of the game.

Position: Machine Learning EngineerAbout Us:At UnitX, we are pioneering the development of cutting-edge physical AI systems designed to automate repetitive visual tasks within manufacturing environments. Our dynamic startup thrives on a diverse team of experts from renowned institutions such as Stanford, MIT, and Google. To date, we have successfully implemented over 1,000 mission-critical AI systems across more than 190 of the world's top manufacturing production lines. Annually, our AI inspection systems oversee the quality of products valued at $15 billion.Join us for a unique opportunity to contribute to groundbreaking computer vision technologies that are transforming global manufacturing efficiency.Your Responsibilities:Design and implement innovative algorithms to analyze raw sensor data for defect detection, focusing on pixel-level precision in high-resolution image and 3D data segmentation.Develop robust software solutions that operate continuously on production lines, executing our algorithms in real-time with decision-making latency under 20ms.Create metrics and tools for comprehensive model performance evaluation, enhancing system visibility and interpretability.Research and explore novel methodologies, pushing the boundaries of AI technology, including Stable Diffusion and SAM, to deliver critical applications in manufacturing.Who You Are:Bachelor's degree in Computer Science, Mathematics, Physics, or a related technical discipline, or equivalent experience showcasing solid mathematical foundations.A minimum of 2 years of experience developing machine learning models focused on computer vision applications in production settings.Deep understanding of Deep Learning theories and practical applications, with proficiency in frameworks such as PyTorch or TensorFlow. Strong Python programming skills for creating efficient, maintainable solutions within extensive codebases.Excellent communication and decision-making abilities, able to articulate experimental rationale and judiciously navigate between exploration and exploitation strategies.Demonstrated resilience and adaptability in complex, uncertain environments.Preferred Qualifications:Experience with large-scale data processing and algorithm optimization.Familiarity with tools for machine learning and data visualization.

Join Hive as a Senior Machine Learning Engineer and help shape the future of AI! We are seeking passionate individuals who excel at developing and deploying cutting-edge deep learning models. In this role, you will work with large-scale datasets to create innovative machine learning solutions, collaborating closely with a talented team of engineers to push the boundaries of artificial intelligence. Ideal candidates will have a proven track record of building and scaling machine learning projects from conception to production, along with a strong commitment to continuous learning and personal ownership in their work.

Be Part of the Future of Autonomous RoboticsAt Bedrock Robotics, we are pioneering the transition of AI from theoretical frameworks to practical applications in the built environment. Our team is comprised of seasoned professionals who have been instrumental in the success of innovative companies such as Waymo, Segment, and Uber Freight. We are at the forefront of deploying autonomous technologies in heavy construction machinery, significantly enhancing the efficiency and safety of multi-billion dollar infrastructure projects across the nation.With backing from $350 million in funding, our mission is to address the urgent need for housing, data centers, and manufacturing facilities, while simultaneously responding to the construction industry's labor shortages.This position is where cutting-edge algorithms meet the practical world of construction. You will work alongside industry experts and top-tier engineers to tackle complex real-world challenges that cannot be simulated. If you are eager to leverage advanced technology for impactful problem-solving within a skilled team, we encourage you to apply.

Join Known as a Founding Machine Learning EngineerLocation: San Francisco, CA (In-Person)Salary: $200,000 - $375,000 Cash + EquityAt Known, we are revolutionizing the way people connect by utilizing advanced AI technology. Our mission is to enhance human relationships through intelligent matchmaking.Users engage with our AI voice assistant, sharing their personal stories for an average of 27 minutes, which provides us with a rich, multi-modal data set.Our team comprises seasoned engineers behind some of the most successful AI-driven consumer applications, including Uber Eats, Uber, Faire, and Afterpay.We value hard work, autonomy, and ownership, and collaborate in our Cow Hollow office in San Francisco.Explore More About Us:Download KnownOur Launch AnnouncementTechCrunch Feature on Our Seed FundingNew York Times Article on KnownFastCompany Review of KnownVisit Our Website

Company Overview At Specter, we are pioneering a software-defined "control plane" designed to enhance the real-world perception of physical assets. Our mission begins with safeguarding American businesses by providing them with comprehensive insights into their physical environments.To achieve this, we are developing a robust hardware-software ecosystem leveraging multi-modal wireless mesh sensing technology. This innovation allows us to significantly reduce the cost and time involved in sensor deployment by a factor of ten. Ultimately, our platform aims to serve as the perception engine for businesses, facilitating real-time visibility and autonomous management of their operational perimeters.Our co-founders, Xerxes and Philip, are deeply committed to empowering our partners in the rapidly evolving landscape of physical AI and robotics. We are a dynamic, rapidly expanding team comprised of talent from Anduril, Tesla, Uber, and the U.S. Special Forces.Position Overview Specter is seeking a dedicated Machine Learning Infrastructure Engineer to construct and optimize the ML systems that drive real-time perception and inference capabilities across our edge-cloud platform. This position will involve overseeing the training, deployment, and enhancement of computer vision and sensor fusion models, aimed at enabling autonomous monitoring and decision-making for our clients' physical assets.Key Responsibilities Include:Design and implement scalable ML training pipelines for computer vision applications, including object detection, tracking, classification, and segmentation.Develop efficient model serving infrastructures to facilitate real-time inference on edge devices with limited computational and power resources.Optimize models for deployment on embedded hardware, employing techniques such as quantization, pruning, TensorRT, ONNX, and CoreML.Create continuous training and evaluation systems to enhance model performance through feedback loops derived from production data.Establish data pipelines for the ingestion, labeling, versioning, and management of extensive multi-modal sensor datasets, including video, radar, lidar, and thermal data.Implement model monitoring frameworks, A/B testing methodologies, and performance analytics for deployed perception systems.Collaborate with perception researchers to transition models from research environments to scalable production across thousands of edge nodes.Construct tools and infrastructure for distributed training, hyperparameter optimization, and experiment tracking.

Join Reducto as a Machine Learning Evaluation Engineer where you will play a critical role in assessing and enhancing machine learning models. You will collaborate closely with data scientists and engineers to ensure our systems are efficient and accurate, bringing innovative solutions to challenging problems in the machine learning space.

Saris AI, based in San Francisco with teams in Montreal and Toronto, develops advanced agentic AI systems for the banking industry. The company focuses on automating complex workflows that require long-context reasoning, integration with legacy systems, and strict compliance. With live AI agents already supporting real customer operations, Saris AI is expanding quickly and seeking technical leaders who want to shape the future of work in banking. Role overview This is a hands-on leadership position within the core engineering team in San Francisco. The Machine Learning Engineering Lead will guide machine learning systems from initial concept through scaling, helping define both the technical vision and the supporting infrastructure. What you will do Oversee the ML/AI function end to end, setting technical direction and standards across the company. Design and supervise development of multi-modal, agentic AI systems that power live customer workflows. Build and manage evaluation frameworks, datasets, and metrics to improve agent performance. Drive productionization of ML systems with an emphasis on reliability, scalability, and compliance. Recruit, develop, and mentor a high-performing ML team, fostering strong practices in modeling, experimentation, and deployment. Requirements 8+ years of experience in machine learning or AI engineering, including time as a technical lead or manager. Proven track record leading ML projects from concept to production deployment. Expertise with large language models (LLMs) and/or agentic systems, especially in customer-facing products. Strong grasp of ML fundamentals: deep learning, transformers, model evaluation, and trade-offs. Hands-on experience scaling ML systems in production, with a focus on monitoring, iteration, and reliability. Ability to lead engineering teams, influence architecture, and set technical direction. Comfort working in early-stage, ambiguous, and rapidly changing environments.