Top Tech Jobs & Startup Jobs in Boston, MA

Own the architecture, roadmap, security, and operation of Lila’s global multi-site network spanning corporate, laboratory, and OT environments. Design SD-WAN/SASE, firewalls, segmentation, remote access, monitoring, and IT/OT convergence. Lead migrations, incident escalations, vendor and MSP management, technical standards, runbooks, and network engineering capability development. Mentor engineers and advise leadership on reliability, security, cost, and delivery tradeoffs.

Own the architecture and roadmap for enterprise endpoint management across macOS, Windows, Linux, mobile, and research environments. Establish configuration-as-code, zero-touch provisioning, security integration, vulnerability management, observability, staged rollouts, and automated remediation. Build tooling and automation, support complex endpoint issues, mentor engineers, and lead initiatives across IT, Security, Research Computing, and Lab Operations while improving device security and employee productivity.

Lead security investigations for AI systems used in scientific discovery. Develop threat models, test defenses through adversarial assessments and technical analysis, define security requirements, evaluate evidence, and drive improvements with Security, Safety, and engineering teams. The role addresses dangerous misuse, model and data compromise, deployment risks, and safeguards for increasingly capable AI systems.

Build and maintain software, services, data pipelines, and model integrations for AI safety evaluations, red teaming, mitigation experiments, and scientific AI research. Translate researcher needs into reliable tools, preserve reproducible experiment records, and own systems from prototyping through deployment, testing, monitoring, documentation, and improvement. Collaborate with AI and software engineering teams on shared infrastructure.

Develop and implement safety mitigations for frontier scientific AI models, agents, tools, and laboratory-connected workflows. Translate risk assessments and policy requirements into safeguards spanning training, classifiers, monitoring, access controls, and runtime interventions. Design experiments, evaluate robustness and legitimate-use impacts, investigate failures and distribution shifts, and integrate mitigations with model training, inference, platform, and evaluation teams.

Own Coupa procure-to-pay configuration through implementation and go-live. Coordinate workshops, configure approvals, catalogs, punch-outs, and supplier onboarding; manage data migration, UAT, reconciliation, defect resolution, and systems integrator activities. After launch, triage issues, implement configuration or training fixes, and deliver documentation and handover to internal owners.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Build provenance infrastructure for scientific data and workflows, including schemas, metadata systems, Python libraries, APIs, and durable records of agent activity. Support reproducible ML, simulation, and drug discovery workflows while transforming research prototypes into scalable production services. Establish engineering practices for testing, packaging, CI, releases, and documentation across local, cloud, and GPU environments. Collaborate closely with scientists and platform teams on storage, compute, and orchestration systems.

Own end-to-end mechanical design of custom mobile manipulators by integrating robot arms with AMR/AGV bases. Create CAD models, mounting structures, cable routing, simulation-ready geometry, and mass, payload, and center-of-gravity analyses. Iterate multiple design concepts, document tolerances and assembly procedures, and support physical builds. Collaborate with simulation and platform evaluation engineers during validation. This is a hands-on mechanical and mechatronic design role focused on hardware integration rather than robotics software or controls.

Build and adapt AI models for customer-specific scientific workflows. Lead post-training using SFT and reinforcement learning, design evaluation systems, analyze model failures, and drive iterative quality improvements. Partner with AI researchers and software engineers to integrate reliable model capabilities into production workflows. The role requires strong Python and PyTorch skills, deep familiarity with language, multimodal, or agentic models, and the ability to translate customer needs into technical solutions.