{"id":857835,"url":"https://alion.io/job/longshotsystems-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":679886,"name":"Longshot Systems","domain":"longshotsystems.co.uk","url":"https://alion.io/company/longshotsystems","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":107000,"max_usd":191000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":49},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Plotly","optional":false},{"name":"Polars","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"SciPy","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"C++","optional":true},{"name":"CI/CD","optional":true},{"name":"Dagster","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"HPC","optional":true},{"name":"Kubernetes","optional":true},{"name":"Linux","optional":true},{"name":"Pandas","optional":true},{"name":"Scikit-learn","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true}],"status":"live","first_seen_at":"2026-10-12T17:52:52Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-11-05T23:17:18Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":23},"description":"At Longshot Systems we build advanced platforms for sports betting analytics and trading.\nWe're hiring Machine Learning Engineers across our core ML engineering and horse racing teams. You'd be designing, building and productionising ML pipelines, tooling, visualisation, frameworks and data engineering workflows to support strategy research, analysis and development, working closely with our quantitative research teams to turn prototype trading models into production-ready systems. You'd also help shape the high-level architecture of our strategy software so it scales effectively and keeps trading latency low. Our ML stack is Python based and utilises modern ML libraries and tooling including Numpy, Scipy, Pytorch, Polars, Ray, Plotly, Dash etc.\nThe ideal candidate will have a strong software engineering background with a track record of building and maintaining production-grade ML pipelines. We are looking for engineers who are comfortable designing robust data engineering workflows, building reliable tooling, and writing clean, maintainable Python code. You should be proficient in modern Python ML and data processing libraries, with a focus on building systems that are scalable and easy to support. Knowledge of common ML algorithms is a plus, but your primary strength should be in software design and productionisation.\nWe are a hybrid working company, working Thursdays in our London (Farringdon) office and flexible the rest of the week. Our typical working hours are 10 am to 6 pm UK time, Monday to Friday, but we support flexible working and trust our team to manage their own schedules to meet their goals.\nOur interview process is as follows:\nIntro call (30 mins) - learn more about your background + discuss the role\nTechnical interview - Python software engineering assessment\nFull assessment day (10:00-5pm) - a one day programming exercise designed to be similar to the real work we do in the team\nRequirements\nA degree in a quantitative, technical subject (e.g. Machine Learning, Maths, Physics, Computer Science etc) from a top university\nSignificant software engineering skills and experience, especially on the modern Python ML stack\nTakes pride in engineering excellence and encourages best practice in others\nStrong experience designing and maintaining ML pipelines and data engineering workflows\nFamiliarity with modern engineering practices such as CI/CD, containerisation (e.g. Docker, Kubernetes) and automated testing\nExperience with cloud platforms (e.g. AWS, GCP or Azure)\nComfortable working in a Linux environment\nNice to have:\nAdvanced data engineering experience in Python, e.g. with libraries like Dagster, Prefect etc\nExperience optimising dataframe code, e.g. in Pandas or ideally Polars\nExperience of machine learning techniques and related libraries and frameworks e.g. scikit-learn, Pytorch, Tensorflow etc\nExperience deploying and serving ML models in production, including model monitoring and real-time inference\nExperience in scientific computing with other languages & frameworks\nStrong general high performance computing (multi-threading, networking, profiling and optimisation)\nExperience with C/C++\nBenefits\nParticipation in the uncapped company bonus scheme\n10% matched pension contributions\nPrivate healthcare insurance\nLong term illness insurance\nGym 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