Remote Sr. Applied Machine Learning Engineer - SearchGrabJobs • New York, New York, United States

Dieses Stellenangebot ist nicht mehr verfügbar

Remote Sr. Applied Machine Learning Engineer - Search

GrabJobs

New York, New York, United States

New York, New York, United States

Über

About us: Let’s be real, AI isn’t magic; Legion was built to move beyond AI hype—delivering secure, reliable systems that work alongside the people tackling the world’s most critical challenges. Born from a Department of Defense partnership and trusted by leaders across government and enterprise, Legion embeds intelligence inside complex systems, unlocking data, accelerating human workflows, and strengthening mission-critical systems. We don’t replace workflows—we optimize them, ensuring quality, efficiency, and reliability inside the platforms our partners already use. With world-class collaborators like Palantir, Nvidia, HPE, and Oracle, we’re building intelligent infrastructure that enhances human capability and drives impact at the edge and across a range of enterprises. We’re looking for bold thinkers and doers to join us in shaping the future of AI that’s secure, grounded, and built to work. Job Summary: Are you an experienced Applied Machine Learning Engineer who thrives in a fast-paced, collaborative team? We seek a highly skilled Senior Applied Machine Learning Engineer to join our Applied ML team. In this role, you will leverage your expertise in AI/ML engineering to design, develop, and deploy innovative machine learning and algorithmic solutions. If you are adept at building models that solve hard problems, we encourage you to apply. You will collaborate closely with platform engineers and product partners, bringing a strong product orientation to your ML work. Responsibilities: Lead the development of machine learning models and data-driven algorithms for high impact projects Collaborate with product and platform teams to own ML solutions end-to-end Understand the runtime complexity of algorithms and the cost to run ML models at a production scale Clearly communicate modeling decisions, tradeoffs, and limitations to technical and non-technical stakeholders Build, deliver, and maintain enterprise products, ensuring they meet high-quality standards while shipping fast Take ownership of your work, from design to implementation and maintenance, and drive projects to successful completion Support production systems, handling debugging challenges in distributed systems to ensure reliability and uptime Adapt quickly, bringing in latest developments in AI and machine learning, and proactively apply this knowledge to drive innovation within the company Required Skills and Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, NLP Minimum 5+ years of experience in applied machine learning, with a track record of delivering enterprise products Proven experience in Generative AI, including fine-tuning, optimizing, and evaluating LLMs, RAG pipelines, and Agentic AI systems Strong proficiency with AI/ML/NLP techniques, resources and methodologies (ex: Huggingface, Spacy, Scikit-learn, Pytorch). Strong communication skills and the ability to collaborate effectively with cross-functional teams Comfortable supporting production systems and debugging challenges in distributed systems Demonstrated effectiveness in using AI tooling to accelerate research and development workflows Growth mindset and low ego- you’re eager to pick up new tools and technologies, learn from others, and being open to changing course when it’s right Preferred Qualifications: Start-up experience or comfort in 0→1 product environments. Familiarity with Kubernetes is a plus. Experience integrating machine learning models and data driven algorithms into larger system architectures. Compensation Information
$205,000 - $260,000 USD

New York, New York, United States

Sprachkenntnisse

English

Hinweis für Nutzer

Dieses Stellenangebot wurde von einem unserer Partner veröffentlicht. Sie können das Originalangebot einsehen hier.

Ähnliche Jobs finden