Top Machine Learning Engineer Jobs

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Lead the development of ML product infrastructure, create a scalable platform for ML solutions, collaborate with teams, and mentor new engineers.

Lightfield seeks a Machine Learning Engineer to create innovative AI experiences, collaborate on AI/ML strategy, develop product features, and recruit team members. Must have 5+ years in AI/ML and NLP.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Design and implement production-grade AI features to enhance recruiting efficiency, utilizing AI/ML technologies for intelligent candidate search and insights extraction.

Develop and implement machine learning models for evaluating disputes and chargebacks, and create a model orchestration layer. Collaborate on data use for enhanced user experiences.

Join Sajix as a Machine Learning Engineer Trainee to assist in building and deploying ML models for healthcare solutions, collaborating with data scientists, and gaining hands-on experience.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

The role involves developing ML-based planning algorithms for autonomous trucks, ensuring safety checks, real-time operation, and keeping up with research. Requires strong communication and collaboration.

Lead a small team to research, develop, train, optimize, and deploy computer vision and multimodal fusion AI/ML models for remote sensing (EO/SAR). Analyze large datasets, translate research into operational systems, present technical findings to customers, and produce supporting documentation and user guides.

Design, create, and implement AI and ML systems for Defense and Intelligence sectors, ensuring scalability and integration into various environments.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Lead post-training for LLMs (CPT, SFT, RL) focusing on RL methods; design and curate training and reward data; run and debug distributed training on mid-to-large GPU clusters; build evaluation and verifier pipelines; partner with product and business teams to deliver targeted model improvements quickly.

Design and build the intelligence layer: retrieval and memory systems, RAG pipelines, feedback and evaluation loops, RL and fine-tuning experiments, LLM integration, observability for AI performance, and collaborate to productize AI features.

Design and lead data science solutions: ingest and analyze structured and unstructured data, build predictive models and NLP systems, integrate enterprise tools, and advise clients while guiding teammates on AI/ML workflows and deployments.

Design and lead end-to-end AI solutions using LLMs, RAG, agentic systems, vector databases and cloud infrastructure. Advise on AI architecture, MLOps, production readiness, and guide engineering teams to align systems with business goals.

Lead and grow a team of 4-5 ML engineers to design, build, and operate enterprise-grade, distributed AI/ML systems (LLM serving, retrieval, agentic frameworks). Provide architectural direction, establish ML lifecycle CI/CD and evaluation frameworks, implement safety/traceability guardrails, and ensure responsible AI and data governance compliance.

Design, build, and operate scalable enterprise AI/ML systems including LLM infrastructure, RAG pipelines, and autonomous agent frameworks. Provide architecture and code-level leadership, establish ML engineering best practices, implement safety guardrails and traceability, develop evaluation frameworks, and standardize CI/CD and lifecycle processes while ensuring compliance with data governance and responsible AI standards.

Lead development of production agentic AI systems: reasoning, planning, tool orchestration, memory and grounding, evaluation frameworks, and multimodal agent coordination across chat, voice, and UI. Translate research into reliable, testable systems and iterate with founders and customers.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Lead development of perception algorithms for robotic welding using RGB, LiDAR, and ToF sensors. Oversee research on image processing, point cloud and 3D sensor fusion, design and evaluate deep learning models, optimize deployable perception systems, mentor junior engineers, and drive architecture and strategic decisions for production-ready robotics perception.

Build and own an end-to-end drawing-parsing pipeline: ingest PDFs/CAD, perform layout analysis, detection, OCR, segmentation, extract metadata and schedules, design embedding strategies, integrate into a knowledge store, and build evaluation/ground-truth systems while partnering with engineering staff and the CTO to productize models for production use.

Build production systems that probe and modify LLM internals to improve reliability and enforce deterministic policy. Implement mechanistic-interpretability techniques (activation patching, control vectors), work with weights/activations, build evaluation and deployment loops, and design feature-level intervention systems for enterprise inference.

Designs, implements, and deploys cloud-native AI/ML solutions and data pipelines to support personalization and optimization. Builds and serves ML models (real-time and batch), develops APIs and front-end tools, automates workflows, and ensures secure, scalable AWS infrastructure. Leads architecture discussions, mentors engineers, manages project lifecycle, and advises senior management on technical strategy and risk.