{"id":668636,"url":"https://alion.io/job/imc-trading-hardware-machine-learning-engineer","title":"Hardware Machine Learning Engineer","company":{"id":54502,"name":"IMC Trading","domain":"imc.com","url":"https://alion.io/company/imc-trading","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":87,"open_postings":139,"ghost_share":0.137,"stale_share":0.007,"repost_share":0,"time_to_fill_p50_days":108,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chicago, United States","New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":225000,"currency":"USD","period":"year","gross":null,"usd_annual":225000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Apache TVM","optional":true},{"name":"C++","optional":true},{"name":"Cocotb","optional":true},{"name":"MLIR","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Quantization","optional":true},{"name":"SystemVerilog","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true},{"name":"UVM","optional":true},{"name":"VHDL","optional":true},{"name":"XLA","optional":true}],"status":"live","first_seen_at":"2026-02-13T18:35:18Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-06T17:13:06Z","board_verified":true,"closed_at":null,"days_open":265,"trust":{"level":"ok","repost_count":1,"flags":["company_stale"],"days_open":265},"description":"We are deploying machine learning directly onto custom hardware - and we want you to help drive it from the ground up. This is an initiative where you'll have the rare opportunity to architect solutions from scratch, influence technical research direction, and see your work drive real impact in one of the most demanding computing environments in the world.\nWe build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can fix it - there's no vendor to wait on and no abstraction layer you're not allowed to touch. If you've ever wanted to push the boundaries of what's computationally possible, this role is for you. We're looking for researchers and experienced engineers from any background. Trading experience is a bonus, not a prerequisite.\nYour Core Responsibilities\nArchitect 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\nShape our custom hardware roadmap by translating ML model requirements into concrete architectural decisions\nWork hands-on with hardware engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production\nTrack and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems\nYour Skills and Experience\nSolid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs\nExperience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLStools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI\nUnderstanding of machine learning fundamentals - neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow\nProficiency in Python, C++, or similar languages for tooling, testing, and simulation\nStrong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams\nNice to Have\nExposure to ML compiler infrastructure such as MLIR, TVM, XLA, or similar tools for lowering and optimizing models for hardware targets\nBackground in latency-sensitive or resource-constrained systems including high-frequency trading, particle physics data acquisition, real-time signal processing, or similar domains\nFamiliarity with functional verification methodologies (for example SystemVerilog, UVM, Cocotb)\nAdvanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent depth through industry or research experience\nThe 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.\nSalary Range\n$200,000—$225,000 USD\nAbout Us\nIMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. 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