Technical Program Manager, R&D & Technology Transfer
Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control by designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms for high precision manipulation of complex or deformable objects. Work with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.
2026 New Grad | Software Engineer, Full-Stack
Ship critical infrastructure managing real-world logistics and financial data for large enterprises. Own the why by building deep context through customer calls and understanding Loop's value to customers, pushing back on requirements if better solutions exist. Work full-stack across system boundaries including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to handle routine tasks enabling focus on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise velocity.
Member of Technical Staff - Post Training, Applied
The role involves acting as the technical owner for enterprise customer post-training engagements, owning post-training projects end-to-end from customer requirements through delivery and evaluation. Responsibilities include translating customer requirements into concrete post-training specifications and workflows, designing and executing data generation, filtering, and quality assessment processes, running supervised fine-tuning, preference alignment, and reinforcement learning workflows, as well as designing task-specific evaluations, interpreting results, and feeding learnings back into core post-training pipelines.
Senior Machine Learning Engineer - Payments
As a machine learning engineer on the core ML payments team, you will design, build, and deploy scalable machine learning solutions and systems. You will experiment with new modeling approaches and strategies, collaborate closely with a team of engineers on ingesting signals, and productionize these models. Your work will empower millions of users through well-known and emerging Fintech applications with access to financial services. Responsibilities also include working on both 0-1 stage problems and 1-10 stage problems, developing AI/ML models through the full lifecycle from offline training to online serving and monitoring, collaborating with teams across the company to define the ML roadmap, and applying data-driven decisions in day-to-day work in a high ownership, bottom-up driven team.
Machine Learning Engineer - Perception Mapping (copy)
As a software engineer on the perception mapping team at Zoox, you will curate, validate, and label datasets for model training and validation. You will research, implement, and train machine learning models to perform semantic map element detection and closely collaborate with validation teams to formulate and execute model validation pipelines. You will integrate models into the greater onboard autonomy system within compute budgets. Additionally, you will serve as a technical leader on the team, maintaining coding and ML development best practices and contributing to architectural decisions.
Research Engineer/Scientist - Generative UI, Consumer Products
As a Research Engineer/Scientist on the Consumer Products Research team, you will train and evaluate state-of-the-art models along important axes for future devices, work through challenges to transform nascent research capabilities into usable capabilities, and help define software frameworks for long-term future use.
Senior Research Engineer/Scientist - Edge, Consumer Products
As a Research Engineer/Scientist on the Consumer Products Research team, you will train and evaluate multimodal state-of-the-art (SoTA) models along axes important to the vision for future devices. You will develop novel architectures that improve model performance when scaling the models themselves is not an option. You will also work to transition nascent research capabilities into capabilities that can be built upon.
Software Engineer
Design, develop, and maintain web applications and backend services that integrate ML-powered features. Collaborate closely with Machine Learning Engineers and Product Managers to understand ML system requirements and translate them into robust software solutions. Build reliable, scalable, and low-latency services that support ML inference, data workflows, and AI-driven user experiences. Use LLMs to build scalable and reliable AI agents. Own the full software development lifecycle: design, implementation, testing, deployment, monitoring, and maintenance. Ensure high standards for code quality, testing, observability, and operational excellence. Troubleshoot production issues and participate in on-call or support rotations when needed. Mentor junior engineers and contribute to technical best practices across teams. Act as a strong cross-functional partner between product, engineering, and ML teams.
Applied AI, Evaluation Engineer
Design and implement comprehensive evaluation frameworks to measure LLM capabilities across diverse customer use cases including text generation, reasoning, code, and domain-specific applications; build scalable evaluation infrastructure and pipelines that enable rapid, reproducible assessment of model performance; develop novel evaluation methodologies to assess emerging capabilities or verticalized use cases such as cybersecurity, finance, and healthcare; create custom evaluation suites tailored to enterprise customers' specific needs while working closely with them to understand their requirements and success criteria; collaborate with research teams to translate evaluation insights into model improvements and training decisions; partner with product teams to continuously improve evaluation tooling based on customer feedback.
Applied AI, AI Engineer for Mistral
The Applied AI engineer at Mistral works within the customer-facing technical team to deploy AI solutions that deliver measurable business impact. Responsibilities include identifying high-value internal use cases across various departments such as engineering, legal, HR, sales, and operations; building end-to-end LLM applications including prompts, RAG pipelines, APIs, simple UIs, deployment, and monitoring; owning the full lifecycle of AI tools from prototype to production, maintenance, and iteration; documenting learnings and sharing insights with product and research teams; and converting successful internal tools into customer demos or case studies where appropriate. The role also involves acting as the first internal customer for these tools to identify edge cases and limitations, improving models through usage feedback.
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