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.

Role Description We are looking for a Principal Machine Learning Engineer to shape the future of machine learning across Prodege’s Performance Marketing business. This is a high-impact role for someone who wants to own more than models. You will build and evolve the production ML systems that drive outcomes across: - Ranking - Rewards - ROAS / LTV prediction - Offer optimization - Experimentation - Decisioning Your work will directly influence revenue, margin, user value, and marketplace efficiency in a fast-moving AdTech / MarTech environment. This is a deeply hands-on principal role. We are looking for someone who leads by building, shipping, and operating production ML systems; not someone who stays only at the architecture or strategy layer. You will own the ML stack end to end, from: - Problem framing - Feature strategy - Model development - Experimentation - Deployment - Observability - Lifecycle optimization You will build production ML systems for a business serving 120M+ registered users that has delivered $2B+ in lifetime rewards, powered by a data platform with: - 50M events per day - 500M records of daily pipeline throughput - 100TB Iceberg lake - 50 Kafka topics and growing across batch and real-time workflows If you enjoy building real-world ML systems, working close to the business, and helping a team move toward a more AI-first engineering model, this role is for you. Qualifications - 8+ years of experience in software engineering, machine learning engineering, MLOps, or related technical fields - 5+ years building, deploying, and supporting production ML systems at scale - Strong experience in AdTech, MarTech, Growth, Performance Marketing, or adjacent domains - Strong hands-on background in ranking, recommendation, rewards/incentives, ROAS/LTV prediction, personalization/optimization systems - Proven experience designing, shipping, and operating production ML systems end to end - Strong understanding of offline/online ML architecture, feature engineering, model serving patterns, experimentation frameworks, A/B testing, MLOps, retraining, monitoring, and governance - Strong system design skills with sound judgment across performance, reliability, scalability, and cost - Strong technical leadership and mentoring capability Requirements - Ability to guide teams toward an AI-first way of working - Comfort operating in ambiguity and still driving systems into production - Experience partnering closely with Data Engineering / BI / Analytics teams Benefits - Comprehensive benefits package including medical, dental, vision, STD, LTD, and basic life insurance - Flexible PTO and paid sick leave prorated based on hire date - Eight paid holidays throughout the calendar year Pay Transparency The anticipated base salary range for this position is $300,000 to $375,000. The final salary offered to a successful candidate will be dependent on several factors that may include, but are not limited to; the type and length of experience within the job, type and length of experience within the industry, the type and length of knowledge and skills for the position, education, training, etc. Prodege is a multi-state employer and final compensation within this range could be impacted by work location. Equal Employment Opportunity Statement At Prodege, we are committed to creating a diverse and inclusive environment. We are proud to be an Equal Opportunity Employer and do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by law. We encourage individuals of all backgrounds to apply.

Role Description We are looking for an MLOps Engineer to build, scale, and operate the critical systems that power Sequen’s AI models in production. This is a foundational, purely infrastructure-focused role sitting at the intersection of machine learning, backend distributed systems, and platform performance. You will not be client-facing; instead, your primary customer will be our internal ML research scientists. Your mission is to make model serving, evaluation, and scaling completely seamless, reliable, and highly optimized in high-throughput production environments. Key Responsibilities - Build ML infrastructure: - Design, operate, and maintain robust systems for low-latency model deployment, distributed inference pipelines, and automated real-time telemetry. - Scale ranking systems: - Move models cleanly from experimentation to production, optimizing the critical trade-offs between execution latency, GPU/CPU throughput, and cloud infrastructure costs. - Implement model CI/CD: - Build reliable infrastructure for automated model versioning, canary releases, hot-swappable container rollouts, and zero-downtime rollbacks. - Drive system observability: - Architect and monitor real-time pipelines to track model performance, data distribution drift, and system reliability anomalies. - Develop evaluation loops: - Engineer robust evaluation pipelines and feedback loops to continuously validate live inference accuracy and prevent training-serving skew. - Optimize platform bottlenecks: - Proactively isolate and eliminate performance bottlenecks across our serving layers, improving core tooling, model warm-up times, and researcher velocity. - Collaborate with research: - Partner closely with our internal ML researchers and backend engineers to translate experimental model breakthroughs into resilient, production-grade serving topologies. Qualifications - Bring 4–8+ years of practical experience in MLOps, Machine Learning Engineering, or distributed platform/infrastructure engineering. - Demonstrate hands-on experience deploying and serving ultra-low-latency machine learning models under heavy, real-time concurrent workloads. - Maintain deep, production-grade proficiency with Python and PyTorch. - Operate comfortably across major cloud platforms (AWS, GCP, or Azure) utilizing modern containerization and orchestration tooling (Docker, Kubernetes). - Show experience designing robust, scalable data pipelines, model registries (e.g., MLflow), and automated CI/CD infrastructures. - Bring a solid, first-principles understanding of the complete machine learning lifecycle, asynchronous event-driven patterns, and distributed systems. Strong Candidates May Also Bring - Bring production experience or active, hands-on familiarity with Rust for low-overhead systems engineering. - Exposure to serving and optimizing large language models (LLMs) or large-scale generative model architectures (vLLM, Triton). - Familiarity with enterprise-grade feature stores, advanced experiment tracking, and systematic model evaluation frameworks. - Prior experience building and scaling software infrastructure from scratch in fast-moving, early-stage, or hypergrowth startups. What We Value - You balance algorithmic complexity with microsecond runtime latency constraints. - You treat production stability and platform efficiency as a personal reflection of code quality. - You possess the startup velocity to design, deploy, and validate robust infra prototypes quickly. - You act as a technical multiplier for our research scientists, building clean developer interfaces and automated workflows. What We Offer - A foundational, high-autonomy role directly shaping the core deployment and serving topology of a category-defining AI infrastructure company. - The unique opportunity to build and scale category-defining, low-latency ML platforms backed by proven, highly quantified customer revenue results. - Highly competitive base salary, uncapped performance metrics, and meaningful early-employee equity. - Full premium medical/dental/vision coverage, unlimited paid time off, and a highly collaborative, world-class engineering culture.