Senior Machine Learning Engineer

RiseMe; Palo Alto (CA); On-site; USD 190,000 - 320,000; Full time

Job summary

RiseMe is seeking a Senior Machine Learning Engineer to architect the next phase of our ML platform and enhance developer experience for model creation. You will lead hands-on efforts from design docs to implementation and customer-facing delivery, shaping end-to-end pipelines and serving production models.

You will guide evaluation, benchmarking, platform strategy, and collaboration across product, engineering, and UX teams, while staying ahead in RL post-training and open-source developments

Qualifications

• 2+ years of experience building models with real-world data.
• Experience in VC-backed startups or scaling ML solutions at large tech companies.
• Expertise in selecting agent harnesses and integrating ML solutions for workflows.
• 5+ years of software development with technical leadership experience.

Responsibilities

• Architect the end-to-end ML platform and developer experience.
• Develop, fine-tune, and benchmark models for enterprise use cases.
• Lead design reviews and help plan quarterly product roadmap.
• Maintain platform reliability with on-call rotations and monitoring.
• Collaborate with product managers, engineers, and designers.
• Stay current with RL post-training, open source trends, and cost efficiency.

Tools

Bun

TypeScript

PostgreSQL

Job description

About the Role

Our next Senior Machine Learning Engineer will work on architecting the next phase of our core ML platform, the developer experience of creating models, and dogfood building models on our platform with clients. This is a hands‑on tech‑lead role where you’ll get to meet with customers and design their model pipeline from design docs and ideation to implementation and serving traffic.

Key Responsibilities

ML Model Development

Experiment, develop, and create finetuned models that specialise in specific enterprise use cases for clients. For example, taking a generic customer workflow that uses SaaS tools and mapping it into an agent. The work will blend LLM post‑training (SFT and RL‑based optimisations), identifying the right harness, embedding retrieval, and similar to run the end to end for the client. Finally, handing the pipeline to the client for long‑term ownership.

Evaluation & Benchmarking

Build the evaluation harnesses and benchmarks that determine when a specialised model is production‑ready. Balance the customisation for end use cases with platform level features that are usable across teams.

Technical Leadership & Strategy

Design the end‑to‑end ML Platform that powers development and optimises the platform to maximise growth of models trained and hosted with us. Help drive the developer experience for customers and product roadmap with quarterly planning. Lead thorough and fair design doc reviews for new features.

Platform Excellence

Monitor production systems built on our Bun/TypeScript backend and Postgres, troubleshoot issues, and ensure high availability SLAs through effective debugging and on‑call rotations.

Collaboration & Innovation

Translate product requirements into scalable ML designs by collaborating with product managers, full stack engineers, and UX designers. Stay ahead of trends like RL post‑training, state of the art in open source, and sandbox platforms to keep our stack cost effective.

Continuous Improvement

The ML stack moves quickly, and our products evolve with it. Knowing what to build and how to build it requires raising the bar of the entire team. You’ll help set a culture of continuous learning by providing fast feedback to individual team members, and encouraging experimentation within the team. Individually we expect you’ll regularly be exploring the latest industry trends and the means of how to stay up to date, from conferences to podcasts, NeurIPS papers, and endless scrolling on X.

Nice‑to‑Have Skills

• (Highly Preferred) Familiarity with reinforcement learning for post‑training.

• Design and creation of coded reward verifiers for a variety of model use cases and class sizes

• Development of simulators for reward calculation in single or multi turn environments

• Familiarity with inference using quantised model artefacts; single endpoint multi‑model serving directly on GPUs

• Familiarity with trading off build vs buy managed services, like Together AI or Fireworks.

Qualifications

• 2+ years of experience of building models. Including manipulating raw data and workflow examples from end‑specific applications into formatted training sets, then fine‑tuning LLMs with sensible benchmarks to compare improvement against base and proprietary models.

• Background in VC‑backed startups or scaling ML solutions at large tech companies. (0→1) experience

• Expertise in identifying appropriate agent harnesses, model candidates, and integrating numerous ML solutions for specific workflows.

• 5+ years of professional experience in software development, including a portion in tech lead roles