Responsibilities
- Contribute to one or more scientific capability areas end to end, for example perception, computer vision, language and agents, time series, control, planning, or evaluation
- Take problems from ambiguous product or system requirements through clear research questions, hypotheses, and success metrics
- Contribute to applied research projects: literature review, method selection, experimentation, ablation, error analysis, and productization
- Build and run the evaluation pipelines for the work you own: offline metrics, online experiments, robustness testing in industrial conditions
- Work with engineers to take models into production grade pipelines: data readiness, training infrastructure, inference, observability
- Make scientific tradeoffs in front of engineers and product managers, with evidence, and translate them into decisions the team can act on
- Identify and de-risk scaling challenges in your area: data quality, model drift, latency, throughput, cost, safety
- Raise the bar on experimentation rigor, reproducibility, and documentation across the team
- Apply responsible AI practices in your work: bias detection, model risk management, human in the loop controls
Basic qualifications
- 4+ years in applied machine learning, AI research, or data science, with models that shipped to production and made an impact
- Strong foundation in machine learning theory and practice across training, evaluation, and deployment
- Demonstrated experience taking research from a paper or prototype into a model that runs reliably in production
- Proficiency in Python and modern ML frameworks and toolchains
- Strong partnership track record with engineering teams on data, training infrastructure, and inference
- Clear written and verbal communication with engineers, product managers, and senior leaders
Preferred qualifications
- Experience applying ML in industrial or physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare
- Deep expertise in one of: multimodal ML, generative AI, retrieval augmented generation, agentic workflows, time series, control, or planning
- Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML, geometry-aware ML, differentiable simulation, AI for semiconductor/EDA
- Hands-on experience building evaluation pipelines, running online experiments, or instrumenting production monitoring for a model you owned
- Publications, patents, open source contributions, or significant internal technology transfers
- Experience working with globally distributed research, product, or engineering organizations
About the company
We are an early-stage engineering team solving hard technical problems at the intersection of AI, systems, and real-world interaction. We operate with high autonomy, collaborate closely across functions, and maintain high technical standards. Principal Engineers at our company are expected to lead by example, model engineering excellence, and shape both what we build and how we build it.
Our Commitment to Equity and Inclusion in our Diverse Global Workforce:
We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the everyday with us.
$182,600 $216,700 10%