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Machine Learning Manager (LLM)

Blue Rose Research · Work from home
SeniorWork from home
PayPay not listed
WhereWork from home
TypeFull timeManager
Posted25 Sep11 days ago, via Himalayas
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Machine Learning ManagerData Science ManagerLLM EngineeringApplied AIMachine Learning EngineerMachine Learning ManagementAI ML ManagementMachine Learning Architecture ManagerML ManagementMachine Learning Program ManagerLLM Solutions ManagerMachine Learning Platform Management
About this job

About us

Blue Rose Research builds data and AI tools that help Democrats win elections. Our team combines engineering, data science, and political strategy to power decisions for the country’s top campaigns and progressive organizations. We forecast elections, test ads, and use generative AI to help campaigns understand what’s happening in the news; then respond fast with messages that actually work. We have guided how hundreds of millions of dollars are spent in modern campaigns. We’re a small, mission-driven team that builds fast, experiments boldly, and helps progressives communicate and win—guided by curiosity, purpose, and a genuine desire to use technology for good.

Machine Learning Lead (LLM & Applied AI)

We’re looking for a Machine Learning Lead for a small team of senior data scientists who are developing ML-driven products that power data-informed strategy for civic leaders and organizations. Reporting to the Director of Engineering, you’ll be in charge of the roadmap and technical direction. This is a hands-on role. You'll be collaborating with the team to build the infrastructure, train the models, and deploy them to production. If you’re motivated to use your technical expertise for meaningful, mission-driven work that advances the public good, this role offers the chance to make a tangible impact.

Other Responsibilities Include

Lead a team of senior data scientists focused on fine-tuning large language models, conducting cutting-edge R&D, and building production inference systems.

Collaborate with senior leadership to define the team roadmap and align priorities with organizational goals.

Lead weekly meetings and standups, keeping the team unblocked and execution moving forward.

Provide technical direction across projects using open-weight and off-the-shelf LLMs, as well as other advanced ML techniques.

Oversee experimentation, optimization, and data quality to ensure models are accurate, reliable, and production-ready.

Foster creative problem-solving and methodological rigor when challenges require custom solutions beyond standard ML approaches.

Translate complex model outputs into actionable insights for stakeholders, ensuring technical work drives real-world impact

About you

1+ years leading data science teams; 6+ years in ML or data engineering.

Strong background in applied statistics, model selection, tuning, and evaluation.

Proficient in Python, SQL, and modern ML frameworks (PyTorch, TensorFlow, or JAX).

Experienced in building and deploying production ML and deep learning pipelines.

Familiar with LLMs, embeddings, agentic workflows, and RAG systems.

Comfortable with cloud and DevOps tools (Docker, Kubernetes, Terraform).

Skilled in exploratory data analysis and handling imperfect real-world data.

You’ll thrive in a fast-moving environment where priorities evolve quickly and impact is immediate.

Collaborative leader who communicates clearly with technical and nontechnical teams.

Mission-driven, curious about civic and political applications of AI, and fosters a positive team culture.

What we Offer

Salary
$165,000 - $210,000 annually, commensurate with experience
Benefits
Competitive medical, dental, and health coverage
Work Environment
Remote-first, with offices and regular meetups in NYC and DC (primarily East Coast hours)
Culture
Fast-moving, collaborative team doing innovative work with real-world impact
Growth
Opportunities to learn new skills, take on challenges, and shape meaningful projects
Inclusion
We welcome applicants from diverse backgrounds — you don’t need to meet every qualification to apply
Eligibility
Candidates must be authorized to work in the U.S.

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