Forward Deployed Engineer responsible for translating customer needs into research and engineering solutions, building prototypes, and deploying Odyssey’s world models in real-world environments.
Responsibilities
Embed closely with strategic customers to understand workflows and constraints
Identify opportunities for world models across robotics, simulation, autonomy, training, interactive media, and other domains
Translate customer goals into research and engineering problems, prototypes, and deployment plans
Adapt and extend Odyssey’s models through fine‑tuning, post‑training, conditioning, controllability, inference optimization, and learned control policies
Build end‑to‑end prototypes connecting models to customer data, tools, simulators, hardware, and production systems
Build and lead AI systems that transform innovative models into secure, reliable, and scalable capabilities for scientists across Pfizer.
Compensation
The annual base salary for this position ranges from $139,100.00 to $231,900.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 17.5% of the base salary and eligibility to participate in our share based long term incentive program.
Responsibilities
Own an end-to-end subsystem, platform capability, or workflow
Make sound architecture and design tradeoff decisions
Deliver production-quality software and AI solutions
Drive technical execution for complex projects
Influence engineering standards and best practices
Requirements
PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical discipline with 2+ years of relevant experience
Master's degree with 5+ years of relevant experience
Bachelor's degree with 6+ years of relevant experience
Build and deliver production AI systems that enable scientific discovery at Pfizer.
Compensation
The annual base salary for this position ranges from $106,000.00 to $176,600.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 15.0% of the base salary and eligibility to participate in our share based long term incentive program.
Responsibilities
Own end-to-end subsystem, platform capability, or workflow
Make sound architecture and design tradeoff decisions
Deliver production-quality software and AI solutions
Drive technical execution for complex projects
Influence engineering standards and best practices
Requirements
PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or related discipline
Master's degree with 2+ years of relevant experience
Bachelor's degree with 4+ years of relevant experience
Nice to have: Experience in life sciences, healthcare, biotechnology, or highly regulated industries
Forward Deployed ML Engineers work at the intersection of deep technical work and direct customer impact, partnering with leading AI companies and foundation model labs to achieve state-of-the-art performance on workloads like LLM serving, model training, and audio pipelines.
Responsibilities
Work hands-on with companies like Suno, Cognition, and Meta to architect and optimize production AI workloads on Modal
Contribute to open-source projects (e.g., SGLang) and publish technical content
Collaborate with Modal’s product and sales teams, contributing to the platform as both an engineer and a product stakeholder
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proofs of concept
Requirements
Nice to have: Side projects
Nice to have: open-source contributions
Nice to have: published work in ML or systems performance
Partner with AI-native founders and engineering teams to design, optimize, and scale AI platforms on NVIDIA technologies, focusing on agentic systems, AI copilots, and multimodal AI.
Compensation
expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Serve as a trusted technical advisor to the next generation of AI-native companies building category-defining applications such as developer copilots, AI search engines, agentic platforms, and multimodal creative tools.
Work directly with startup founders and engineering teams to architect and optimize AI workloads using NVIDIA technologies including CUDA-X libraries, TensorRT-LLM, Triton Inference Server, NVIDIA NeMo, NIM microservices, and GPU-accelerated data processing frameworks.
Help AI-native companies scale training and inference infrastructure, optimizing model performance, cost efficiency, and latency across NVIDIA accelerated computing platforms.
Guide startups through complex architectural decisions including model optimization, inference scaling, agent frameworks, multimodal pipelines, and real-time AI systems.
Collaborate closely with NVIDIA engineering, research, product, and go-to-market teams to identify emerging AI-native categories and influence NVIDIA’s platform roadmap.
Requirements
BS/MS degree or equivalent experience in Computer Science, Engineering, or a related field
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Serve as a trusted technical advisor to the next generation of AI-native companies building category-defining applications such as developer copilots, AI search engines, agentic platforms, and multimodal creative tools.
Work directly with startup founders and engineering teams to architect and optimize AI workloads using NVIDIA technologies including CUDA-X libraries, TensorRT-LLM, Triton Inference Server, NVIDIA NeMo, NIM microservices, and GPU-accelerated data processing frameworks.
Help AI-native companies scale training and inference infrastructure, optimizing model performance, cost efficiency, and latency across NVIDIA accelerated computing platforms.
Guide startups through complex architectural decisions including model optimization, inference scaling, agent frameworks, multimodal pipelines, and real-time AI systems.
Collaborate closely with NVIDIA engineering, research, product, and go-to-market teams to identify emerging AI-native categories and influence NVIDIA’s platform roadmap.
Requirements
BS/MS degree or equivalent experience in Computer Science, Engineering, or a related field
Nice to have: building or scaling AI-powered products
Architect and co-design ML models with traders, quant researchers, and software engineers, treating hardware constraints as first-class design inputs; shape custom hardware roadmap; work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions; track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods.
Locations (2)
Chicago, Illinois, United States
New York, United States
Compensation
The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.
Responsibilities
Architect and co-design ML models with traders, quant researchers, and software engineers, treating hardware constraints (latency budgets, resource limits, numerical precision) as first-class design inputs
Shape our custom hardware roadmap by translating ML model requirements into concrete architectural decisions
Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production
Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems
Requirements
Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through industry or research experience
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