• Be responsible for the architecture of hybrid game and machine learning systems. • Design, develop and maintain learning and non-learning systems. • Assist programming and machine learning teams throughout development phases. • Spread machine learning knowledge across the organization. • Improve the function of your team.

Role Description Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey. Position: MLOps Engineer (JAX, PyTorch, Pallas/Triton) — IN Type: Contract Compensation: $35–$45/hour Location: Remote Commitment: 40 hours/week - Guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics. - Design challenging, domain-relevant tasks, and write accurate and well-structured solutions to MLOps and ML systems problems. - Evaluate MLOps tasks and solutions and provide clear, written technical feedback. - Develop guidelines and detailed rubrics/evaluation frameworks to assess training pipeline design, distributed systems reasoning, and kernel-level optimization across tasks. - Collaborate with other subject matter experts to ensure consistency and accuracy in training data. Qualifications - Must-Have: 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a recognized, top-tier organization. - Hands-on production experience with JAX and/or PyTorch at scale. - Experience writing or optimizing custom GPU kernels using Pallas (JAX) or Triton. - Demonstrable career progression. - Ability to engage reliably for at least 30 hours/week during weekdays. - Strong written communication skills and the ability to explain complex technical decisions clearly. Requirements - W-2 employment with Cincinnatus LLC. - Paid weekly. Application Process - Upload resume - AI interview based on your resume - Submit form For details about the interview process and platform information, please check: Interview Process For any help or support, reach out to: support@mercor.com PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

Role Description We're seeking a Machine Learning Engineer to lead the development of state-of-the-art ML systems that personalize and optimize credits and refund decisions. This work is critical to balancing cost efficiency with long-term customer retention and experience. In this high-impact role, you will partner with cross-functional leaders to design and deploy causal models and optimization algorithms that influence millions of user experiences every week. You’re excited about this opportunity because you will: - Design and deploy causal inference models to accurately assess the impact of refunds and credits on customer satisfaction, retention, and behavior. - Develop optimization frameworks that balance customer experience with operational cost, under policy and budget constraints. - Build personalized decision systems that adapt to customer preferences and platform dynamics in real time. - Collaborate with engineering, product, and data science partners to shape the roadmap for trust, service recovery, and consumer experience. - Lead end-to-end model development, including experimentation, deployment, monitoring, and iteration. Qualifications - 3+ years of industry experience delivering machine learning systems with clear business impact, especially in personalization, optimization, or causal inference. - Deep expertise in statistical modeling and causal inference (e.g., uplift modeling, treatment effect estimation, synthetic controls, instrumental variables). - Experience designing and deploying optimization algorithms (e.g., multi-objective optimization, bandits, constrained optimization). - Proficiency in Python and ML tooling such as PyTorch, Spark, and MLflow. - A strong product sense and ability to translate business objectives into technical solutions. - M.S. or Ph.D. in a quantitative field (e.g., Computer Science, Statistics, Operations Research, Economics, Mathematics). - Excellent communication skills and a track record of cross-functional leadership. Benefits - Comprehensive benefits package including a 401(k) plan with employer matching. - 16 weeks of paid parental leave. - Wellness benefits and commuter benefits match. - Paid time off and paid sick leave in compliance with applicable laws. - Medical, dental, and vision benefits. - 11 paid holidays. - Disability and basic life insurance. - Family-forming assistance and a mental health program. - Flexible paid time off/vacation for salaried roles, plus 80 hours of paid sick time per year. - Vacation accrued for hourly roles at about 1 hour for every 25.97 hours worked. - Paid sick time accrued at 1 hour for every 30 hours worked. Company Description At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more. We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives.

Role Description We are hiring a Senior Machine Learning Engineer (LLM Engineer) to lead the design, implementation, and productionization of advanced ML and LLM-powered systems. This is not a research-only role. This is a client-facing, production-focused leadership role. - Architect and deploy LLM and agentic AI systems in production - Lead ML solution design across customer engagements - Mentor engineers and influence technical direction - Ensure scalable, maintainable, enterprise-grade ML systems Qualifications - 7+ years of ML / AI / Data Science experience - 5+ years AWS hands-on experience - Deep expertise in machine learning and advanced analytics - Strong experience with NLP and LLM technologies - Mastery of Python and strong SQL proficiency - Proven leadership in customer engagements - Experience shipping ML systems to production Requirements - Experience building agentic systems - MLOps pipeline ownership - Enterprise consulting experience - Experience across multiple industries - Exposure to vector databases and RAG systems Company Description Client delivers enterprise-grade Data and AI consulting solutions for mid-market and enterprise clients across multiple industries. The team partners with globally recognized clients to design and deploy production-grade AI systems with measurable business impact.