Senior/Staff ML Research Engineer

Explore opportunities across the globe in The Machine Learning EcoSystem.

This organization is dedicated to rebuilding the industrial base by deploying advanced AI and automation across heavy industries like utilities, logistics, and manufacturing. As a member of a high-impact engineering team, the AI Engineer will build production-level agentic products that solve complex challenges in the physical world, from predictive maintenance to autonomous operations planning. The role requires a hands-on approach to developing multimodal systems that process video streams and IoT sensor data. Successful candidates will thrive in an early-stage environment, moving quickly from prototype to deployment while maintaining the high reliability required for industrial applications.

• Architect and deploy agent runtime and orchestration systems for vision quality and operations planning.
• Take ownership of multimodal perception pipelines, including video frame extraction and cross-sensor correlation for anomaly detection.
• Contribute to the development of industrial knowledge graphs and ontologies that ground agent reasoning.
• Build the platform memory layer to facilitate compounding intelligence and knowledge transfer across various deployments.
• Develop operational interfaces, including real-time dashboards and replanning consoles for enterprise clients.
• Integrate AI systems with industrial software like ERP and CMMS by creating connectors for heterogeneous data.

• At least 3 years of professional experience as an ML, Computer Vision, or Software Engineer, with 5+ years preferred.
• Proven track record of shipping production AI systems from zero to one, specifically agentic products.
• Strong proficiency in Python and TypeScript.
• Experience handling complex sensor data or video streams, with a preference for backgrounds in robotics or autonomous vehicles.
• Ability to work autonomously in a fast-paced, industrial-focused startup environment.

This organization provides a specialized platform designed to train AI agents within high-fidelity simulation environments. By focusing on experiential learning rather than simple instruction, they enable autonomous agents to master complex enterprise tasks. As part of a lean, high-performing team of eight, the Senior AI Software Engineer will play a central role in scaling the core simulation engine and backend infrastructure. The successful candidate will own the development of features that allow customers to deploy and refine AI agents. This role requires a balance of sophisticated backend engineering and practical implementation of Large Language Model (LLM) tooling.

• Design and develop robust backend services using Python and FastAPI.
• Integrate and manage data stores including PostgreSQL, Redis, and various vector databases.
• Implement and iterate on AI agents within secure sandbox environments.
• Manage cloud infrastructure and DevOps tasks, including Infrastructure as Code, containerization, and cost optimization.
• Collaborate closely with research and product teams to meet project milestones.
• Maintain high code quality through rigorous testing, metrics instrumentation, and CI/CD management.

• Four to six years of experience in backend-focused software engineering.
• At least one to two years of experience shipping production-level LLM features, such as agents or chatbots.
• Deep proficiency in Python and familiarity with modern frameworks like FastAPI.
• Working knowledge of Kubernetes, Terraform, and major cloud providers (AWS, GCP, or Azure).
• A proactive approach to ownership and the ability to operate in a fast-paced environment.

A leading innovator in the financial technology space is seeking an Engineering Leader for Applied ML to help shape the future of autonomous accounting. This role sits at the heart of the Applied ML team, which develops sophisticated systems capable of reading documents, reasoning over complex contexts, and completing high-stakes accounting workflows with precision. The team prioritises a holistic systems approach rather than focusing solely on model development. This involves optimising orchestration, retrieval, and memory while pushing model providers to their limits through custom runtimes and unconventional loops. As a leader, responsibility extends beyond technical architecture to building a sustainable, high-performing engineering culture that thrives on rigor and rapid iteration.

• Build and scale the Applied ML organization by hiring, coaching, and mentoring a world-class team of engineers.
• Define and evolve multi-agent architectures, focusing on autonomy boundaries, safety layers, and context management.
• Own the evaluation infrastructure to ensure models are shipped with confidence, traceability, and stability.
• Collaborate with Research, Product, and Platform teams to translate technical insights into production-grade systems.
• Establish clear standards for experimentation, observability, and documentation to maintain a high bar for engineering craft.

• At least 7 years of experience in software engineering, with a significant focus on leading machine learning or applied AI teams.
• A deep understanding of building and managing production-grade ML systems and agentic workflows.
• Strong architectural intuition and the ability to think in systems while debating complex technical trade-offs.
• Experience fostering a culture of continuous learning, fast iteration, and objective evaluation.

This organization is dedicated to restoring transparency and information quality across the internet. As the world's leading AI text identification platform, they serve over 11 million active users by providing tools that preserve and enhance critical thinking. The company is seeking an AI Engineer to join their machine learning team in New York City as a foundational member during a period of rapid growth. The role focuses on building and deploying reliable AI agents into production environments. You will work closely with a highly accomplished leadership team, including a CTO with multiple ML patents and an AI Research Lead with deep industry experience. This position offers a unique chance to work on hallucination detection and responsible AI adoption tools that directly impact how students and educators interact with technology.

• Design and build reliable AI agents and deploy them into production using Python and various APIs.
• Utilize prompt engineering and optimize backend model parameters like temperature and input/output structure.
• Implement Retrieval-Augmented Generation (RAG) and other advanced frameworks to enhance model performance.
• Develop tools for hallucination detection to promote the responsible use of AI.
• Collaborate with design and research leads to translate machine learning research into intuitive user-facing products.

• 2 to 6 years of experience in full-stack or machine learning roles with a focus on building agentic AI applications.
• Strong proficiency in Python and familiarity with tools such as Langchain, LlamaIndex, DsPY, or TextGrad.
• Proven ability to move software from development into a production environment for a large user base.
• A solid understanding of how Large Language Models operate on the backend.
• Passion for building software that prioritises information integrity and transparency.

This company is building the AI-native operating engine for the wholesale supply chain, a sector seven times the size of e-commerce that still relies on outdated manual processes. Having recently secured a $3+M, the team is now seeking a Founding Engineer to own the development of their autonomous agent layer. This role is central to replacing manual spreadsheets and email chains with intelligent systems that manage the entire order-to-cash lifecycle for high-growth brands. As a Founding Engineer, the focus is on architecting and deploying specialized agents that operate within complex supply chain environments. These systems must coordinate across ERPs, third-party logistics providers, and retailer portals with high precision.

• Architect agentic systems to handle end-to-end ordering, freight tendering, and reconciliation.
• Develop robust tool-use, retrieval, and planning mechanisms with production-grade safety guardrails.
• Optimize models through fine-tuning, evaluation frameworks, and human-in-the-loop feedback systems.
• Collaborate directly with customers to translate intricate operational workflows into automated solutions.
• Work within a modern tech stack including TypeScript, Python, Node.js, and emerging agentic frameworks.

• 3 to 8 years of experience building and scaling ML or AI products in production environments.
• Strong proficiency in Python or TypeScript and experience with agentic frameworks and AI tools.
• A high-agency mindset with the desire to build greenfield technology from the ground up.
• Ability to maintain zero-tolerance for error while deploying systems into real-world supply chain infrastructures.

This company is reimagining the digital dating experience. By moving beyond traditional swipes to create a service that prioritises genuine resonance, the team is building a platform where quality matters more than quantity. With a focus on intentionality and craft, the company offers a collaborative environment in the heart of New York City for those who enjoy solving complex problems in person. The AI Engineer will play a pivotal role in designing and maintaining the orchestration systems that power core features. This role involves taking full ownership of the evaluation infrastructure, ensuring AI quality at scale. Successful candidates will work at the intersection of product and research to ship scalable, reliable, and user-facing AI capabilities.

• Design and maintain prompts, structured outputs, and orchestration workflows.
• Build and scale evaluation infrastructure using tools such as Langfuse, TensorZero, and Promptfoo.
• Lead the development of internal dashboards and debugging workflows to monitor system behavior.
• Run structured experiments across models to optimize for quality, cost, and latency.
• Implement opportunities to fine-tune open-source models using SFT, DPO, and RLHF techniques.

• Between 3 to 8 years of total experience in machine learning, data, or software engineering.
• At least 1 to 2 years of experience specifically building and deploying LLM-powered products in production.
• Proven track record of monitoring and maintaining AI applications over time.
• Proficiency with Python, FastAPI, and PostgreSQL.
• Experience with recommendation systems or consumer-facing products is highly regarded.