• Technical leader and individual contributor in Machine Learning • Build and ship models to help customers search for financial products • Develop and deploy advanced ML models to 15 million customers • Involve user and product embeddings and personalised ranking algorithms • Support experimentation and innovation to solve customer problems

Role Description We are looking for a talented Machine Learning Engineer to join our team and work on innovative projects for our clients. Our mission is to help organizations unlock the full potential. - ML Systems & Deployment - Design and build scalable model training, deployment, and inference pipelines - Deploy models into production using CI/CD, containerisation, and cloud infrastructure - Ensure models meet performance, latency, and reliability requirements - MLOps & Lifecycle Management - Implement model versioning, monitoring, and automated retraining mechanisms - Detect and respond to model drift, data drift, and performance degradation - Establish operational standards for AI systems - Collaboration with Data Science & Product - Partner with Data Scientists to productionise and scale models - Work with Product and Engineering teams to integrate ML into customer-facing products - Translate experimental work into robust production assets - Governance, Security & Ethics - Ensure ML systems comply with privacy, security, and regulatory requirements - Support explainability, auditability, and responsible AI practices - Document models and operational decisions clearly Qualifications - 8+ years experience in ML engineering, MLOps, or software engineering roles - Strong software engineering skills in Python and cloud environments - Hands-on experience with ML platforms (MLflow, Kubeflow, SageMaker) and Kubernetes - Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field - MS in Big Data, AI or relevant fields Benefits - Flexible employment and remote work - International projects with leading global clients - International business trips - Non-corporate atmosphere - Language classes - Internal & external training - Private healthcare and insurance - Multisport card - Well-being initiatives

Role Description As a Staff Machine Learning Engineer, you will be a team lead on the Employer Agents Team. Your team will be responsible for driving value and better customer experiences for our employer-facing Agentic solutions, helping redefine the employer hiring journey through AI. You will own one of the team's major workstreams, partner with cross-functional teams to develop and deliver ML and AI projects, including: - Agentic solutions - LLMs-as-a-Judge - Evaluation capabilities - ML systems and models You will help drive technical direction for the team while guiding other members to achieve product and technical goals. On a daily basis, you will: - Explore data - Formulate problem statements - Build new agentic experiences - Drive improvements in our LLMOps reliability and infrastructure Qualifications - Requires a Bachelor’s degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 8 years of related experience; or a Master’s degree with a minimum of 6 years of experience; or a PhD with 3 years experience - Familiarity with agent orchestration frameworks, LLM observability tools, and prompt optimization techniques (e.g. GEPA) - Prior success in deploying impactful Machine Learning solutions to large-scale production systems, while partnering across teams - Solid knowledge of data structures and algorithms - Sense of ownership and accountability as a key contributor in the technical and product domains - Knowledge and practical experience working on Deep Learning Libraries (like Torch, Tensorflow, etc.) - Excellent written and verbal communication in English, effective with technical and business audiences Requirements - Partner with cross-functional teams to enhance and optimize search algorithms for improved accuracy, relevance, and overall user experience - Experiment with Proof of Concept Machine Learning model improvements, scale them to production, and run iterative A/B experiments to improve our matching technology while partnering with other teams - Define and clarify project priorities, deliverables, and success criteria in partnership with cross-functional teams - Act as a bridge between technical and non-technical collaborators, facilitating effective communication and comprehension of project goals and outcomes - Mentor and grow other software engineers and Machine Learning Engineers across teams - Break down larger Machine Learning initiatives into pieces that deliver incremental business value and guide the team through implementing them - Represent Indeed at major Machine Learning conferences, such as Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the International Conference on Learning Representations (ICLR) Benefits - Quarterly bonuses - Restricted Stock Units (RSUs) - Paid Time Off policy - Region-specific benefits

Role Description Join EvenUp as a Staff Machine Learning Engineer and help set the technical direction for how machine learning powers Piai™, our proprietary claims-intelligence platform. This is a technical leadership role - you'll shape modeling strategy across a broad problem space, turning raw legal and medical data into production systems that improve outcomes for personal-injury clients. You'll partner closely with Product, Research, and Engineering leaders to set strategy, and you'll be a technical anchor for the broader ML team - setting standards, mentoring senior engineers, and driving decisions that shape both product outcomes and company growth. What You'll Do - Set technical strategy for a broad area of the ML roadmap, translating ambiguous business and research goals into scoped, production-ready systems. - Tackle the hardest modeling problems in the org - complex reasoning, long-context and multi-document understanding, or other frontier challenges as they come up. - Apply advanced ML techniques - fine-tuning, reinforcement learning, retrieval, or others - and know when a technique is the right tool versus over-engineering. - Establish rigorous evaluation standards, reducing hallucinations, improving factual consistency, and defining what "good" looks like for a given system. - Drive data excellence through hands-on analysis of training and evaluation data, managing noise, edge cases, and drift at scale. - Provide technical leadership and mentorship across the ML team, raising the bar for experimentation, benchmarking, and engineering rigor. - Act as the bridge between research and production - ensuring new techniques get integrated into shippable systems, not just proofs of concept. - Partner cross-functionally with product, engineering, and legal subject-matter experts to set technical direction. - Cost effectively scale practical machine learning systems in a hyper-growth environment, ensuring they remain grounded in real business and customer needs. Qualifications - 7+ years of hands-on ML engineering experience, with multiple models shipped and running in production. - Deep expertise in ML and NLP, including LLMs, with a track record of solving hard modeling problems - not just applying existing recipes. - High proficiency in Python and strong command of modern ML/NLP frameworks. - Demonstrated ability to set technical strategy and drive execution in ambiguous, fast-moving environments. - A track record of mentoring engineers and raising technical standards beyond your own output. - Experience partnering directly with Product and Engineering leadership, not just executing their asks. Nice to Have - PhD in Machine Learning, Computer Science, or a related quantitative field. - Experience with document understanding, entity/relationship extraction, or structured extraction from unstructured text. - Experience with LLM fine-tuning techniques (LoRA, QLoRA, RLHF/RLVR) or advanced prompt engineering. - Experience in a high-growth startup environment. - Open to remote candidates or 3 days a week hybrid from our Toronto or San Francisco hubs. Benefits - Choice of medical, dental, and vision insurance plans for you and your family. - Additional insurance coverage options for life, accident, or critical illness. - Flexible paid time off, sick leave, short-term and long-term disability. - 10 US observed holidays, and Canadian statutory holidays by province. - A home office stipend. - 401(k) for US-based employees and RRSP for Canada-based employees. - Paid parental leave. - A local in-person meet-up program. - Hubs in San Francisco and Toronto.