Product Design

Software Engineer - Model Developer Ecosystem

Own the developer-facing narrative for the model library by creating guides, tutorials, demos, and reference content to help developers select the right model for their needs. Build and nurture both sides of the model ecosystem by onboarding new models and educating developers. Develop evaluation frameworks and use‑case‑specific criteria beyond benchmark scores to reflect real-world developer requirements. Create community programs, events, and educational initiatives to establish Baseten as the main destination for model discovery and selection.

Collaborate cross‑functionally with product, engineering, and marketing teams to translate developer feedback into product improvements. Represent Baseten externally at conferences, meetups, and community events.

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, including kernel backends, speculative decoding, 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 such as RLHF, RLAIF, GRPO, and DPO‑style methods, making RL and post‑training workloads more efficient with inference‑aware training loops.

Use these pipelines to train, evaluate, and iterate on frontier models. Co‑design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation to efficient inference, identifying bottlenecks across training engine, inference engine, data pipeline, and user‑facing layers. Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, feeding insights back into model, RL, and system design.

Profile, debug, and optimize inference and post‑training services under production workloads. Drive roadmap items requiring engine modification, including changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to rigorously validate improvements. Provide technical leadership by setting technical direction for cross‑team efforts intersecting inference, RL, and post‑training, and mentor other engineers and researchers on full‑stack ML systems work and performance engineering.

Build AI models and features that impact Loop’s business by training, evaluating, and deploying machine learning models, particularly focusing on document extraction and understanding using multimodal large language models (LLMs). Utilize and orchestrate API LLM models to solve business problems, and handle backend engineering tasks including building atomic tasks and general servicing or automation work. Work on projects such as scaling foundation models for document extraction to multiple languages and developing AI agents for auditing freight invoices and ingesting long contracts.

Collaborate with cross‑functional teams and maintain high accuracy and reliability standards in AI model training and inference scaling.

You will own client leadership and growth, ensuring smooth engagement delivery and value for enterprise customers, and manage delivery across multiple work streams, maintaining quality and timelines. You will also identify account expansion opportunities and collaborate cross‑functionally to solve problems and inform product strategy.

Lead the brand’s creative direction at Jasper, managing a team of designers, collaborating with Marketing, and shaping how Jasper shows up in the world. Translate brand positioning into compelling creative expressions across campaigns,…