Top Machine Learning Engineer Jobs

As a Machine Learning Engineer at Onyx, you will enhance agent and knowledge retrieval capabilities by implementing LLM-based graphs and advanced NLP algorithms, while collaborating with the team to improve user experience and product direction.

The role involves developing, training, and deploying machine learning models, building data pipelines, and collaborating with teams for integration and performance optimization. It supports the ML lifecycle from data collection to monitoring.

The role involves designing, developing, and deploying machine learning models for healthcare solutions, optimizing data processing pipelines, and collaborating with cross-functional teams to enhance patient care.

The role involves training and deploying ASR models, improving pronunciation feedback, measuring performance, and expanding ASR systems for language learning.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

Lead design and deployment of generative world models and sensor simulations to produce high-fidelity synthetic datasets for large-scale simulation. Integrate multimodal and diffusion models, advance sim-to-real transfer and domain adaptation, apply VLMs and world-modeling for controllable 4D environments, and establish AI infrastructure and testing frameworks to validate edge cases for autonomous flight systems.

As a Machine Learning Engineer, you will focus on user-behavioral modeling, collaborate with teams, and engage in data processing and model deployment.

As a Machine Learning Engineer at Yobi, you will design and optimize systems for real-time model serving and inference, ensuring performance and reliability.

Design, train, validate, and deploy ML models and production inference pipelines (CV, NLP, geospatial). Build MLOps workflows, scalable APIs, and AWS-deployed AI workloads. Monitor model drift, optimize performance, collaborate with data scientists, architects, and stakeholders, and document processes and integrations.

Lead the design and development of machine learning solutions, overseeing the entire software development lifecycle and mentoring engineers. Drive innovation in Generative AI and high-performance systems.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.

Design, build, and scale retrieval-augmented generation (RAG) systems: chunking, embeddings, vector search, LLM orchestration, and production ML pipelines. Work cross-functionally to deploy robust retrieval, indexing, and evaluation solutions over large proprietary datasets, collaborating with MLOps, Product, and Design to productionize and maintain systems.

Build and maintain production AI/ML applications, backend services, data pipelines, and machine learning models. Develop agentic AI systems, LLM and RAG solutions, and MLOps capabilities including monitoring, versioning, A/B testing, and retraining. Collaborate with product, software, platform, and data teams to deliver reliable, explainable, and performant AI solutions for financial markets.

Design and implement advanced ML models (reinforcement learning, neural nets, metaheuristics) for logistics optimization, predictive analytics, anomaly detection, and real-time decision systems. Lead architecture design for scalable, secure AI systems, mentor teams, align technical roadmaps with defense requirements, and conduct feasibility studies and technology evaluations for contested and uncertain logistics environments.

Develop and deliver production-ready 3D and camera-based perception ML models (BEV emphasis). Own model training, evaluation, debugging, and integration into autonomy systems. Improve training pipelines, tooling, and robustness; lead small technical initiatives and mentor junior ML engineers while collaborating cross-functionally to deploy perception models at scale.

Lead technical direction and implementation of computer vision and multi-modal ML models (transformers, diffusion, VLMs, JEPA) from research to production. Define model architecture, training, evaluation, and deployment across cloud and on-device targets. Mentor engineers, set engineering standards, partner with research and platform teams, and optimize for latency, memory, and cost to deliver scalable AI-driven game experiences.

Optimize and deploy multi-modal models (transformers, diffusion, VLMs) to run on-device across mobile and desktop. Own export, quantization, kernel tuning, runtime integration, and profiling to meet strict latency, memory, and power budgets. Build packaging, benchmarking, and telemetry; partner with research to productionize models and mentor junior engineers.

Lead end-to-end on-device ML inference and integration for real-time game experiences: optimize and export models, tune kernels and runtimes (WebGPU/native), set performance standards, mentor engineers, and build CI/benchmarking and deployment pipelines to meet strict latency, memory, and power budgets.

As a Senior Machine Learning Engineer, you will design and deploy computer vision models, collaborate with teams, and mentor juniors while ensuring high-performance solutions.

The role involves developing ML systems, designing low-latency infrastructure, and collaborating on AI/ML initiatives while ensuring reliability and performance at scale.

Design, build, and operate ML infrastructure for training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, and cost. Create pipelines, observability, benchmarking, and tooling to enable fast experimentation and productionization of models while diagnosing regressions and bottlenecks.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead design, training, and evaluation of multi-modal ML perception models (camera, lidar, radar) for fallback autonomy. Define metrics, curate datasets, implement training and optimized inference pipelines for on‑vehicle constraints, and integrate models into production. Partner with safety, systems, and product teams for validation, verification, and deployment. Mentor engineers and promote ML and software engineering best practices.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Lead on-device ML inference deployment and performance optimization for outdoor camera/doorbell workloads. Optimize latency, throughput, memory, power and stability across CPU/DSP/NPU/GPU. Perform kernel/operator-level tuning, runtime integration in C/C++, quantization validation, and build profiling/benchmarking tooling. Drive cross-functional work with ML, firmware, and hardware teams and provide staff-level technical leadership and mentoring.