Applied A.I. / ML Engineer

Manifold Industries • San Francisco, CA

2d

About The Position

Manifold builds software for people making strategic decisions on behalf of the United States, aiming to enhance the judgment of decision-makers with better tools rather than automating decisions. This role involves building forecasting models for DoD action officers, dealing with complex and often unlabeled data. The engineer will be responsible for the entire lifecycle of the model, from design to MLOps and production integration, utilizing techniques like causal inference and superforecasting. A key aspect of this role is the ability to think in systems, understand how the ML pipeline fits into the broader product and partner's mission, and effectively communicate complex technical work to diverse audiences, including senior government stakeholders. The role presents unique challenges, such as sparse ground truth, the need for explainability, and an evolving mission landscape, which are considered reasons to join rather than deterrents.

Requirements

• Bachelor's degree in computer science, electrical engineering, math, physics, or a related technical field OR equivalent experience shipping production machine learning systems
• Prior experience architecting end-to-end machine learning systems that have been deployed to an end customer
• Hands-on experience across multiple ML paradigms (e.g., classical ML, deep learning, probabilistic methods, GenAI)

Nice To Haves

• Advanced degree in computer science, machine learning, artificial intelligence, or related technical field
• Prior experience building ML models in a startup environment
• Ability to point to writing, talks, or documentation that explains technical work to non-technical audiences
• Prior experience in defense

Responsibilities

• Operate like Forward Deployed Engineers, working with the customer to understand their mission and why their outcomes matter, using these insights to inform architectural decisions.
• Partner with engineers to identify and wrangle public and private datasources for model development, finding substitutes or proposing better alternatives when data is insufficient.
• Hypothesize, test, and compare various model approaches, including causal inference, superforecasting, deep neural networks, and Bayesian methods, evaluating them with quantitative rigor and practical judgment.
• Configure and monitor end-to-end ML systems and MLOps pipelines to proactively catch drift and degradation.
• Architect ML systems that function reliably in production under real-world conditions.
• Monitor models in production, partner with DevSecOps for issue resolution, and collaborate on how predictions are surfaced to end-users.
• Translate complex ML work into clear explanations for diverse audiences, from senior government stakeholders to junior engineers, ensuring understanding and trust.

Benefits

• 401(k) with matching
• Comprehensive medical, dental, and vision coverage for you and your dependents
• Unlimited PTO
• Health & Wellness stipend
• Company-wide break the last two weeks of the year
• Supportive leave of absence including time off for military service, medical events, and parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Senior