Research Program Manager - Model Development
Research Program Managers embed within the pre-training ML and Data teams to deeply understand the technical landscape, build trust with researchers and technical leads, and identify areas where programs and processes can have the most impact on research velocity. They drive end-to-end execution of complex, cross-team research initiatives spanning data, model architecture, training runs, and evaluation, frequently without established playbooks. They coordinate the operational rhythm of pre-training research, including experiment prioritization, run scheduling, data readiness, and checkpoint handoffs to downstream teams. They equip research leadership to make decisions quickly by analyzing technical tradeoffs and presenting clear, actionable recommendations rather than status updates. Managers build lightweight processes that bring structure to unstructured research environments without adding friction, creating coordination mechanisms for cross-team handoffs, configuration management, and research milestones to replace ad hoc communication with durable, visible systems. They act as the connective tissue between pre-training, mid-training, post-training, and infrastructure teams to ensure that upstream decisions propagate cleanly and downstream teams are never surprised.
Research Program Manager - Model Evals and Safety
Research Program Managers at Reflection embed directly with research and infrastructure teams to accelerate frontier model development, focusing on building the foundational infrastructure for model evaluations and safety. They define evaluation frameworks, tooling requirements, and operational processes to assess model capabilities, risks, and release readiness, and establish model safety operations including workflows, review cadences, and decision frameworks. They partner with research and engineering leads across all training phases to embed safety and evaluation checkpoints rigorously and without creating bottlenecks. They drive the scoping and prioritization of evaluation science and infrastructure investments, decide on in-house builds versus adoptions, and engage with the external safety ecosystem, representing the company's safety posture. They create visibility and reporting structures for leadership on model safety status and risks, and champion a culture of blameless post-mortems and continuous learning to improve systems and processes continuously.
Manager, Forward Deployed Engineering - Munich
Lead and grow a team of Forward Deployed Engineers (FDE) delivering production systems with frontier models. Own end-to-end delivery outcomes through clarity, speed, tight coordination, and technical quality. Codify effective practices into tools, playbooks, and roadmap inputs to create leverage for OpenAI and the wider developer community. Identify and raise early indicators with urgency from product behavior, customer environments, or delivery practices. Use judgment to decide when action is required. Set high performance standards for FDEs and support each individual's growth through direct, actionable feedback. Define staffing and support strategies for scalable field teams without added complexity.
Manager, Forward Deployed Engineering - London
Lead and grow a team of Forward Deployed Engineers delivering production systems with frontier models; own end-to-end delivery outcomes through clarity, speed, tight coordination, and technical quality; codify effective practices into tools, playbooks, and roadmap inputs to create leverage for OpenAI and its developer community; notice and urgently raise early indicators related to product behavior, customer environments, or delivery practices; use judgment to determine necessary actions; set high performance standards for FDEs and support individual growth through direct, actionable feedback; define staffing and support structures for scalable field teams without added complexity.
Manager/Sr. Manager, Biopharma Marketing
Lead the team responsible for the AI/ML Stack infrastructure that bridges ML research and large-scale production, evolving the stack to meet scalability needs in ML training and inference workloads. Develop and execute the long-term vision and roadmap for the MLOps team to support ML development and deployment across business units, balancing short-term tactical deliveries and long-term architectural transformation. Manage and mentor a team of 6-7+ engineers, allocate resources strategically to support existing services and strategic initiatives. Collaborate across machine learning, data science, product engineering, and infrastructure teams to identify and address bottlenecks and facilitate deployment of new solutions. Architect compute and storage pipelines to manage large datasets without fragmentation or latency. Modernize the AI product inference stack to support significant growth in AI runs globally. Work with Site Reliability Engineering to establish comprehensive system observability metrics. Conduct build vs. buy assessments and technology stack refresh audits to benchmark and ensure best toolsets are in use.
Chief Technology Officer
The Chief Technology Officer is responsible for defining the long-term architecture for A1's AI systems, infrastructure, and developer platform, evaluating trade-offs between speed of iteration and long-term system design, and ensuring systems are designed for scalability, reliability, and long-term evolution. They guide key decisions across model integration, data pipelines, distributed systems, and product architecture. The CTO works with engineers to translate product direction into clear technical execution, helps structure engineering workstreams and maintain team alignment on priorities, maintains high engineering standards while encouraging shipping, and establishes engineering culture, development practices, and technical standards across the company. They build and scale a world-class engineering team across key talent hubs including China and the US, identify strong technical leaders, define hiring standards and interview processes, and ensure technical workstreams move forward smoothly across teams and locations. The CTO works closely with product, research, and leadership teams and helps resolve cross-team technical and execution challenges.
Chief Technology Officer
The Chief Technology Officer will define the long-term architecture for A1’s AI systems, infrastructure, and developer platform, evaluate trade-offs between speed of iteration and long-term system design, and ensure systems are designed for scalability, reliability, and long-term evolution. They will guide key decisions across model integration, data pipelines, distributed systems, and product architecture. The CTO will work with engineers to translate product direction into clear technical execution, help structure engineering workstreams and keep teams aligned on priorities, maintain high engineering standards while focusing on shipping, and establish engineering culture, development practices, and technical standards. Additionally, they will build and scale a world-class engineering team across key talent hubs including China and the US, identify strong technical leaders, define hiring standards and interview processes, work closely with product, research, and leadership teams, ensure technical workstreams move forward smoothly across teams and locations, and help resolve cross-team technical and execution challenges.
Program Manager, Data Center Delivery
Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines such as SGLang- or vLLM-style systems and Together’s inference stack, including kernel backends, speculative decoding like ATLAS, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post-training pipelines, optimizing algorithms and systems for efficiency where inference constitutes the majority of the cost. Make RL and post-training workloads more efficient with inference-aware training loops, async RL rollouts, and speculative decoding to reduce large-scale rollout collection and evaluation costs. Use these pipelines to train, evaluate, and iterate on frontier models atop the inference stack. Co-design algorithms and infrastructure for tightly coupled objectives, rollout collection, and evaluation with efficient inference, and identify bottlenecks across training engines, inference engines, data pipelines, and user-facing layers. Conduct ablations and scale-up experiments to analyze trade-offs among model quality, latency, throughput, and cost, using insights to inform model, RL, and system design. Profile, debug, and optimize inference and post-training services under production workloads. Lead roadmap efforts that require engine modifications including changes to kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously. Provide technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training and mentoring engineers and researchers in full-stack ML systems work and performance engineering.
Senior Engineering Manager, Reinforcement Learning Environments (RLE)
Lead and grow a high-performing team of 8–9 engineers building reinforcement learning environments. Manage, mentor, and develop senior engineers and future engineering leaders. Partner closely with research, product, and operations teams to define roadmap and execution priorities. Drive technical architecture for scalable, reliable, and extensible environment systems. Build plug-and-play environments that integrate seamlessly with model training pipelines. Balance platform rigor with operational complexity and data quality requirements. Establish engineering best practices around reliability, observability, and performance. Foster a culture of ownership, velocity, and high technical standards.
Senior Manager
Lead transformational AI system implementations by scoping high-value solutions and navigating complex technical challenges alongside technical colleagues. Manage enterprise life sciences accounts, including oversight of pricing, contract negotiations, resourcing, and identifying strategic growth opportunities. Build deep trust with senior stakeholders in global enterprises through understanding how Frontier addresses their operational problems. Advocate for customer needs internally by providing product development teams with direct insights to refine and enhance the platform. Create scalable delivery assets such as playbooks and process improvements to empower external partners and internal teams. Collaborate across functions including engineering, data science, and business development to explore novel use cases and ensure seamless project coordination.
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