As an AI Engineer, you'll build the harnesses, agents, and fine-tuned models that bring generative AI into our products and internal workflows.
Locations (2)
San Mateo, California, United States
New York, United States
Compensation
The base salary (fixed) for this role ranges from $178,500 to $222,000 USD (min to max of base salary). This salary range is applicable to our offices located in California and New York*
Responsibilities
Build a system that turns brand guidelines into ad creatives, then keeps improving them from live campaign performance
Build an agent that speeds up ML research for our Applied Scientists
Turn signals such as creative performance and developer feedback into training data, so our systems keep getting better after launch
Own projects from framing the problem and defining success through shipping and iterating in production
Define what “good” looks like for systems without an obvious metric, and build the evals and annotation processes to measure it
Requirements
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience
Nice to have: Understanding of the ads ecosystem (OpenRTB, SSPs, DSPs, ad exchanges, MMPs)
Nice to have: Contributions to open-source GenAI projects (LLaMA-Factory, verl, opencode, OpenResearch)
Nice to have: Public work in LLMs, diffusion models, reinforcement learning, or agents (publications, write-ups, or talks)
Nice to have: Experience with large-scale GPU training and serving (distributed training and inference optimization)
Develop post‑training methods for forecasting and reasoning, design and execute supervised fine‑tuning, reinforcement learning, on‑policy distillation, and related methods to improve accuracy and calibration.
Responsibilities
Develop post‑training methods for forecasting and reasoning
Design and execute supervised fine‑tuning, reinforcement learning, on‑policy distillation, and related methods to improve accuracy and calibration
Build environments and task distributions
Develop reward modeling and credit assignment methods
Build efficient, reliable training systems
Requirements
Bachelor’s degree or higher in a quantitative or technical discipline
Build, optimize, and scale the distributed training infrastructure for Peano AI’s foundation models, leveraging thousands of NVIDIA GB300/VR200 GPUs and TPU v7x accelerators for high throughput and efficiency.
Responsibilities
Build, optimize, and scale the distributed training infrastructure
Own and improve throughput, efficiency, scalability, cost, and reliability of end-to-end pretraining and RL pipelines
Architect distributed data loading, sharding, checkpointing, batching, and accelerator utilization for multi-node LLM training
Integrate and optimize RL components: rollout, reward modeling, environment orchestration, and mid-training signal injections
Work with and extend frameworks such as Megatron, Transformer-Engine, verl, slime, and other high-performance training libraries
Requirements
Nice to have: experience with large-scale/foundation-model toolkits
Nice to have: familiarity with training pipeline scaling
Nice to have: mixed-precision
Nice to have: memory optimization
Nice to have: deep understanding of supervised and RL-based training cycles
Advance reinforcement learning for large language models, developing, implementing, and running training methods to help models solve frontier-level challenging tasks.
Locations (2)
New York City, New York, United States
San Francisco, California, United States
Compensation
$200,000—$375,000 USD
Responsibilities
Research and implement cutting-edge reinforcement learning algorithms and training recipes for large language models, adapting and extending methods such as GRPO for complex, multi-turn, and long-horizon tasks
Design and build data pipelines that generate high-quality training signals for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks
Prototype and iterate on end-to-end RL training recipes, using training and evaluation results to identify what improves performance and where tasks, environments, or training signals need to change
As a Research Engineer at Percepta, you'll work closely with the core research and product team to build and scale modeling capabilities focusing on large-scale training/inference infrastructure.
Responsibilities
Work backwards from long-term research vision to prioritize the infrastructure and tooling that connects ambition to near-term business value
Rapidly prototype, build, and deploy AI systems that work in the wild with real users and real data
Iterate quickly based on user feedback and partner closely with stakeholders to ensure what you build is used and adopted
Embed directly within customer environments, build trust, and become their embedded partner on applied AI
Encode what you learn into Mosaic, our core product, consistently contributing new features, patterns, and abstractions back into the platform
Forward Deployed ML Engineers work at the intersection of deep technical work and direct customer impact, partnering with leading AI companies and foundation model labs to achieve state-of-the-art performance on workloads like LLM serving, model training, and audio pipelines.
Responsibilities
Work hands-on with companies like Suno, Cognition, and Meta to architect and optimize production AI workloads on Modal
Contribute to open-source projects (e.g., SGLang) and publish technical content
Collaborate with Modal’s product and sales teams, contributing to the platform as both an engineer and a product stakeholder
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proofs of concept
Requirements
Nice to have: Side projects
Nice to have: open-source contributions
Nice to have: published work in ML or systems performance
Work with research scientists, ML engineers, data teams, and delivery teams to diagnose training results, turn findings into actionable improvements to data and environments, and validate those improvements in subsequent training runs
Stay current with advances in large-scale distributed LLM training and scalable RL, and apply relevant methods to our training systems and challenging environments, including tool-use and other complex tasks
Requirements
Ph.D. in machine learning, reinforcement learning, or a related field
AI EngineerNewInMobi · San Mateo, US+1 more
Post-Training Research Scientist - Forecasting and ReasoningNewPoint72 · New York
Founding Mid-Training/RL Infrastructure EngineerPeano AI · Palo Alto, US
Research Scientist - RL TrainingSnorkel AI · New York City, US+1 more
Research EngineerPercepta · New York, US
Forward Deployed Engineer - MLModal · London, United Kingdom