Top Tech Jobs & Startup Jobs in Boston, MA

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

The Senior Backend Engineer will develop core functionalities for AI-driven drug discovery, collaborating with R&D and product teams, managing infrastructure, and ensuring engineering excellence.

The Senior Forward Deployed Engineer will deploy and validate AQNav systems, drive success during tests, communicate with stakeholders, and bridge operations with research and engineering.

Lead product marketing and demand generation for AI Simulation offerings: craft positioning and messaging, produce campaign and sales enablement assets, plan product launches, run outbound and nurture programs, and translate complex technical science into buyer-facing value to drive awareness and pipeline.

Lead product strategy and roadmap for a shared core platform underpinning BioSim and ChemSim. Hire and grow a 5-6 person PM team, drive build-vs-buy decisions, prioritize cross-domain customer needs, establish data-driven operating rhythms and platform OKRs, and stay hands-on during team ramp.

Lead FP&A activities including annual and rolling forecasts, budgeting, variance analysis, program-level financial reporting, and building scalable models and dashboards. Partner with business leaders, operations, GTM, accounting and legal to translate strategy and customer commitments into financial plans, revenue commitments, and program performance metrics while strengthening FP&A controls and automation in a high-growth environment.

The Director of Product Management for AI Generation Engine will lead product strategy and execution in Finance & Risk Modeling, focusing on innovative AI solutions and close collaboration with financial services clients.

The Data Engineer will develop and maintain data pipelines, build data models, ensure data quality, and support cross-functional teams while improving internal data platform infrastructure and documentation.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.