AI Research Engineer Jobs in Boston

Lila Sciences is building Scientific Superintelligence™ to accelerate discovery across medicine, materials, and energy. We are seeking a talented individual contributor to join our AI safety team, focusing on identifying and mitigating risks from scientific superintelligence. This role involves developing and implementing a bespoke safety strategy tailored to Lila's specific goals and deployment strategies. You will work with domain scientists, risk researchers, evaluators, and engineering teams to translate risk findings and policy requirements into effective interventions, spanning research and implementation. The work includes developing approaches, testing their limits, and integrating reliable safeguards into our systems.

$224k - $336k

Lila Sciences is building a talent-dense, high-agency AI safety team focused on identifying and mitigating risks from scientific superintelligence while preserving scientific discovery capabilities. The team's initial focus is to develop and implement a bespoke safety strategy tailored to Lila's specific goals and deployment strategies. This involves technical safety strategy development, broader ecosystem engagement, safety-focused evaluations, safety systems to mitigate risks, and a research agenda exploring long-term needs like overseeing superintelligent scientific systems. We are seeking a materials scientist to map risks across the materials science domain, connecting deep materials expertise with dual-use risk assessment to safeguard scientific AI.

$268k - $384k

DataRobot

DataRobot is seeking a Deep Learning Research Engineer Intern to contribute to the development of a multi-target joint probabilistic foundation model. This ambitious project aims to create a single model capable of handling temporal forecasting, unordered tabular regression and classification, missing-data completion, and mixed-modality inputs and outputs, learning coherent joint structures across various dimensions. The primary focus is on generating real-world value, though strong benchmark performance is also expected. This internship offers a unique opportunity to shape the architecture of a probabilistic foundation model, working on encoders, attention mechanisms, sequence architectures, and distributional output heads. The role requires a blend of deep learning expertise, strong engineering habits, and the ability to reason precisely about model capabilities and limitations.

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