Top AI & Machine Learning Jobs

Own the technical vision and system architecture for Digit’s safety-critical perception systems in dynamic environments. Lead architectural tradeoffs across sensing, ML models, redundancy, latency, compute, and robustness. Define verification and validation standards, support safety cases and hazard analysis, evaluate emerging technologies, and collaborate with navigation, controls, hardware, and safety teams. Mentor senior engineers and represent perception architecture to executives, customers, regulators, and partners.

Develop and evaluate AI/ML-driven automation for ASIC and VLSI design and implementation. Explore GenAI, reinforcement learning, graph neural networks, and convolutional networks to optimize power, performance, area, and quality. Build prototypes, pilot automation on real SoC and IP designs, and collaborate with architecture, design, verification, implementation, academic, and industry teams. Contribute to CAD methodologies, tools, and digital transformation initiatives for advanced automotive processors and microcontrollers.

Architect and develop Java-based AI, IVR, conversational, NLP, and LLM solutions for high-volume customer interactions. Build classification and clustering models, analyze data with SQL, design RAG and prompt-engineering workflows, and lead agentic AI development. Establish LLMOps practices for monitoring, evaluation, and lifecycle management while partnering with platform, speech engineering, and product teams. Mentor junior and offshore engineers.

Develop and optimize large-scale deep learning training, data processing, and production inference systems. Maintain JAX and TPU training infrastructure, improve data quality and evaluation, translate research prototypes into production, optimize speech and language model inference, and resolve performance bottlenecks from kernels through system architecture. Collaborate across research, infrastructure, and production engineering while measuring impact and improving experimental velocity.

Build and integrate enterprise AI solutions using pre-trained LLMs, RAG architectures, traditional machine learning, AI agents, and deterministic logic. Develop backend APIs, React interfaces, data pipelines, and integrations with ServiceNow, SAP, Salesforce, and Azure. Support testing, deployment, monitoring, documentation, and workflow orchestration using LangChain and n8n while collaborating with AI, data, and platform teams.

Leads and mentors data scientists while owning biomedical data science projects from analytical planning through validated delivery. Coordinates project plans, reviews, dependencies, timelines, and cross-functional collaboration. Translates scientific, clinical, product, and commercial questions into analytical solutions; reviews statistical and machine-learning methods, code, validation, and model artifacts. Ensures reproducibility, documentation, quality, and scientific rigor while communicating results to technical and nontechnical stakeholders.

Own the research agenda for Ambral Labs’ replayable enterprise environment engine. Design experiments, build environment factories, develop graders and evaluation sets, mine tasks from historical workflows, optimize models and agent policies, advance long-horizon post-training methods, and scale replay and observability systems. The role combines hands-on research and production implementation while establishing research culture, recruiting talent, and working directly with the CTO on enterprise deployments.

Research and engineer large-scale image and video diffusion models. Train and optimize distributed models on GPU clusters, implement parallelism strategies, profile and debug training infrastructure, improve data and model quality, design experiments and evaluations, and develop fault-tolerant solutions for hardware and numerical issues. The role requires rapid iteration, strong research judgment, custom data pipeline development, and hands-on expertise with PyTorch, distributed training, low-precision computation, and diffusion training across pretraining, preference optimization, and reinforcement learning.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Supports Naval Surface Warfare Center mission engineering by applying data science, statistics, machine learning, data mining, graph algorithms, and operations research to electromagnetic spectrum data. Builds and maintains data pipelines and analytical systems, performs statistical investigations, supports software integration and testing, develops scripts and APIs, and converts large datasets into decision-quality insights.